knitr::opts_chunk$set(tidy=TRUE, tidy.opts=list(width.cutoff=20))
library(tidyverse)
library(janitor)
library(skimr)
library(broom)
library(car)
library(lmtest)
# getwd()
hdi <- read.csv("data/hdi_human_development_index.csv")
broadband <- read.csv("data/broadband_subscribers_per_100_people.csv")
gdp <- read.csv("data/gdp_pcap.csv")
glimpse(hdi)
## Rows: 193
## Columns: 35
## $ country <chr> "Afghanistan", "Angola", "Albania", "Andorra", "UAE", "Argenti…
## $ X1990 <dbl> 0.285, NA, 0.654, NA, 0.713, 0.733, 0.663, NA, 0.867, 0.832, N…
## $ X1991 <dbl> 0.291, NA, 0.638, NA, 0.724, 0.739, 0.652, NA, 0.868, 0.835, N…
## $ X1992 <dbl> 0.301, NA, 0.622, NA, 0.729, 0.743, 0.618, NA, 0.868, 0.841, N…
## $ X1993 <dbl> 0.311, NA, 0.624, NA, 0.736, 0.748, 0.621, NA, 0.872, 0.845, N…
## $ X1994 <dbl> 0.305, NA, 0.629, NA, 0.742, 0.753, 0.625, NA, 0.873, 0.851, N…
## $ X1995 <dbl> 0.329, NA, 0.638, NA, 0.747, 0.754, 0.631, NA, 0.882, 0.856, 0…
## $ X1996 <dbl> 0.334, NA, 0.647, NA, 0.753, 0.760, 0.635, NA, 0.884, 0.861, 0…
## $ X1997 <dbl> 0.338, NA, 0.645, NA, 0.761, 0.768, 0.644, NA, 0.887, 0.865, 0…
## $ X1998 <dbl> 0.338, NA, 0.659, NA, 0.770, 0.772, 0.655, NA, 0.891, 0.870, 0…
## $ X1999 <dbl> 0.347, 0.379, 0.671, NA, 0.780, 0.783, 0.661, NA, 0.893, 0.874…
## $ X2000 <dbl> 0.351, 0.391, 0.682, 0.825, 0.790, 0.789, 0.667, NA, 0.897, 0.…
## $ X2001 <dbl> 0.355, 0.401, 0.691, 0.832, 0.798, 0.795, 0.672, NA, 0.900, 0.…
## $ X2002 <dbl> 0.383, 0.417, 0.697, 0.838, 0.805, 0.795, 0.682, NA, 0.902, 0.…
## $ X2003 <dbl> 0.392, 0.435, 0.705, 0.840, 0.810, 0.804, 0.692, NA, 0.906, 0.…
## $ X2004 <dbl> 0.408, 0.448, 0.710, 0.849, 0.815, 0.808, 0.700, NA, 0.910, 0.…
## $ X2005 <dbl> 0.417, 0.463, 0.723, 0.842, 0.819, 0.812, 0.711, NA, 0.914, 0.…
## $ X2006 <dbl> 0.426, 0.475, 0.732, 0.851, 0.823, 0.822, 0.725, NA, 0.917, 0.…
## $ X2007 <dbl> 0.442, 0.492, 0.742, 0.859, 0.827, 0.825, 0.739, 0.827, 0.919,…
## $ X2008 <dbl> 0.446, 0.504, 0.749, 0.864, 0.831, 0.833, 0.743, 0.830, 0.924,…
## $ X2009 <dbl> 0.458, 0.517, 0.756, 0.866, 0.832, 0.835, 0.741, 0.827, 0.927,…
## $ X2010 <dbl> 0.465, 0.528, 0.769, 0.870, 0.835, 0.844, 0.747, 0.828, 0.929,…
## $ X2011 <dbl> 0.474, 0.545, 0.781, 0.875, 0.839, 0.852, 0.752, 0.828, 0.933,…
## $ X2012 <dbl> 0.484, 0.557, 0.790, 0.876, 0.843, 0.850, 0.761, 0.833, 0.938,…
## $ X2013 <dbl> 0.492, 0.567, 0.793, 0.862, 0.846, 0.852, 0.766, 0.835, 0.935,…
## $ X2014 <dbl> 0.497, 0.577, 0.797, 0.866, 0.853, 0.855, 0.772, 0.837, 0.937,…
## $ X2015 <dbl> 0.496, 0.603, 0.797, 0.869, 0.857, 0.859, 0.777, 0.839, 0.938,…
## $ X2016 <dbl> 0.495, 0.609, 0.797, 0.872, 0.861, 0.857, 0.783, 0.842, 0.939,…
## $ X2017 <dbl> 0.496, 0.610, 0.798, 0.873, 0.884, 0.861, 0.785, 0.845, 0.940,…
## $ X2018 <dbl> 0.498, 0.611, 0.801, 0.875, 0.901, 0.861, 0.789, 0.850, 0.945,…
## $ X2019 <dbl> 0.507, 0.611, 0.805, 0.876, 0.915, 0.861, 0.796, 0.851, 0.947,…
## $ X2020 <dbl> 0.501, 0.610, 0.794, 0.851, 0.909, 0.851, 0.761, 0.840, 0.950,…
## $ X2021 <dbl> 0.486, 0.609, 0.794, 0.871, 0.903, 0.847, 0.786, 0.843, 0.954,…
## $ X2022 <dbl> 0.495, 0.615, 0.806, 0.893, 0.921, 0.858, 0.801, 0.848, 0.952,…
## $ X2023 <dbl> 0.496, 0.616, 0.810, 0.913, 0.940, 0.865, 0.811, 0.851, 0.958,…
glimpse(broadband)
## Rows: 206
## Columns: 27
## $ country <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "UAE",…
## $ X1998 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.10700, N…
## $ X1999 <dbl> NA, NA, NA, NA, NA, 0.01180, NA, NA, NA, NA, 0.63700, NA, NA, …
## $ X2000 <dbl> NA, NA, NA, NA, NA, 0.05650, NA, NA, NA, NA, 2.38000, NA, NA, …
## $ X2001 <dbl> NA, NA, NA, NA, NA, 0.22500, 0.25000, 0.00019, NA, 0.63400, 3.…
## $ X2002 <dbl> NA, NA, NA, NA, 1.73000, 0.42000, 0.38900, 0.00026, NA, 1.3200…
## $ X2003 <chr> "1.52", "", "", "", "5.18", "0.723", "0.674", "0.00033", "", "…
## $ X2004 <chr> "7.47", "0.00085", "", "", "8.45", "1.27", "1.4", "0.0331", "2…
## $ X2005 <chr> "13", "0.0009", "", "0.00884", "13.4", "2.77", "2.36", "0.0662…
## $ X2006 <chr> "14.6", "0.00197", "0.0373", "", "18.4", "4.8", "4", "", "1.91…
## $ X2007 <dbl> 16.60000, 0.00193, 0.05630, 0.33100, 22.60000, 6.75000, 6.5000…
## $ X2008 <dbl> 18.80000, 0.00189, 0.07390, 2.14000, 24.80000, 8.85000, 7.7100…
## $ X2009 <dbl> 19.00000, 0.00364, 0.06690, 3.11000, 27.30000, 10.30000, 8.590…
## $ X2010 <chr> "19.2", "0.0053", "0.0644", "3.6", "30.4", "11.3", "9.76", "3.…
## $ X2011 <chr> "", "", "0.0653", "4.4", "33.1", "12", "11", "5.49", "6.95", "…
## $ X2012 <dbl> NA, 0.00491, 0.08150, 5.50000, 35.00000, 12.70000, 12.20000, 7…
## $ X2013 <dbl> 18.70000, 0.00474, 0.08520, 6.28000, 36.90000, 13.30000, 14.70…
## $ X2014 <dbl> 18.60000, 0.00457, 0.32300, 7.16000, 39.00000, 13.30000, 15.20…
## $ X2015 <dbl> 18.2000, 0.0209, 0.5450, 8.3800, 42.5000, 14.2000, 15.8000, 9.…
## $ X2016 <dbl> NA, 0.0254, 0.2900, 9.1900, 45.0000, 14.4000, 16.5000, 10.2000…
## $ X2017 <dbl> NA, 0.0257, 0.3210, 10.5000, 46.5000, 29.9000, 17.7000, 10.800…
## $ X2018 <dbl> NA, 0.0435, 0.3500, 12.5000, 47.4000, 32.4000, 19.0000, 11.900…
## $ X2019 <dbl> 17.7000, 0.0520, 0.3680, 15.1000, 47.5000, 32.5000, 19.6000, 1…
## $ X2020 <dbl> 17.7000, 0.0680, 0.3630, 17.7000, 48.7000, 34.3000, 21.2000, 1…
## $ X2021 <dbl> 17.7000, 0.0664, 0.3910, 19.6000, 50.3000, 36.5000, 23.1000, 1…
## $ X2022 <dbl> 17.5000, 0.0796, 0.3870, 20.7000, 51.2000, 36.8000, 24.7000, 1…
## $ X2023 <dbl> NA, 0.0801, 0.3740, 22.5000, 51.7000, 37.1000, 25.4000, 18.500…
glimpse(gdp)
## Rows: 193
## Columns: 303
## $ geo <chr> "afg", "ago", "alb", "and", "are", "arg", "arm", "atg", "aus", "…
## $ name <chr> "Afghanistan", "Angola", "Albania", "Andorra", "UAE", "Argentina…
## $ X1800 <dbl> 480.7548, 373.0514, 469.3082, 1370.1511, 1142.3649, 1698.6668, 5…
## $ X1801 <dbl> 480.7548, 374.2587, 470.6541, 1372.4404, 1145.7988, 1704.5903, 5…
## $ X1802 <dbl> 480.7548, 376.0697, 472.0039, 1374.7297, 1150.3774, 1710.5345, 5…
## $ X1803 <dbl> 480.7548, 377.8806, 473.3575, 1378.1636, 1154.9561, 1716.4993, 5…
## $ X1804 <dbl> 480.7548, 379.0879, 474.7151, 1380.4529, 1159.5347, 1722.4850, 5…
## $ X1805 <dbl> 480.7548, 380.8988, 476.0765, 1382.7423, 1162.9687, 1728.4916, 5…
## $ X1806 <dbl> 480.7548, 382.7097, 477.4418, 1385.0316, 1167.5473, 1734.5191, 5…
## $ X1807 <dbl> 480.7548, 384.5207, 478.8110, 1387.3209, 1172.1259, 1740.5676, 5…
## $ X1808 <dbl> 480.7548, 386.3316, 480.1842, 1390.7548, 1176.7045, 1746.6372, 6…
## $ X1809 <dbl> 480.7548, 387.5389, 481.5613, 1393.0441, 1181.2831, 1752.7280, 6…
## $ X1810 <dbl> 480.7548, 389.3498, 482.9423, 1395.3335, 1185.8617, 1758.8400, 6…
## $ X1811 <dbl> 480.7548, 391.1607, 484.3273, 1397.6228, 1189.2957, 1764.9734, 6…
## $ X1812 <dbl> 480.7548, 392.9717, 485.7163, 1401.0567, 1193.8743, 1771.1281, 6…
## $ X1813 <dbl> 480.7548, 394.7826, 487.1093, 1403.3460, 1198.4529, 1777.3043, 6…
## $ X1814 <dbl> 480.7548, 396.5935, 488.5062, 1405.6353, 1203.0315, 1783.5020, 6…
## $ X1815 <dbl> 480.7548, 398.4044, 489.9072, 1407.9246, 1207.6102, 1789.7213, 6…
## $ X1816 <dbl> 480.7548, 399.6117, 491.3122, 1411.3586, 1212.1888, 1795.9623, 6…
## $ X1817 <dbl> 480.7548, 401.4227, 492.7212, 1413.6479, 1216.7674, 1802.2251, 6…
## $ X1818 <dbl> 480.7548, 403.2336, 494.1343, 1415.9372, 1221.3460, 1808.5097, 6…
## $ X1819 <dbl> 480.7548, 405.0445, 495.5514, 1418.2265, 1225.9246, 1814.8163, 6…
## $ X1820 <dbl> 480.7548, 406.8555, 496.9725, 1421.6605, 1230.5032, 1821.1448, 6…
## $ X1821 <dbl> 480.7548, 408.6664, 498.3978, 1437.6856, 1235.0819, 1834.9024, 6…
## $ X1822 <dbl> 481.8440, 410.4773, 499.8271, 1454.8555, 1239.6605, 1848.7640, 6…
## $ X1823 <dbl> 482.9358, 412.2882, 501.2605, 1472.0253, 1244.2391, 1862.7303, 6…
## $ X1824 <dbl> 484.0301, 414.0992, 502.6981, 1490.3397, 1248.8177, 1876.8021, 6…
## $ X1825 <dbl> 485.1268, 415.9101, 504.1397, 1507.5096, 1253.3963, 1890.9802, 6…
## $ X1826 <dbl> 486.2260, 417.7210, 505.5856, 1525.8240, 1257.9750, 1905.2654, 6…
## $ X1827 <dbl> 487.3277, 419.5320, 507.0355, 1544.1385, 1262.5536, 1919.6585, 6…
## $ X1828 <dbl> 488.4319, 421.3429, 508.4896, 1562.4530, 1268.2768, 1934.1603, 6…
## $ X1829 <dbl> 489.5386, 423.1538, 509.9479, 1580.7674, 1272.8555, 1948.7717, 6…
## $ X1830 <dbl> 490.6478, 424.9647, 511.4103, 1599.0819, 1277.4341, 1963.4935, 6…
## $ X1831 <dbl> 491.7595, 427.3793, 512.8770, 1618.5410, 1282.0127, 1978.3265, 6…
## $ X1832 <dbl> 492.8737, 429.1902, 514.3478, 1638.0001, 1286.5913, 1993.2715, 6…
## $ X1833 <dbl> 493.9905, 431.0012, 515.8229, 1657.4593, 1292.3146, 2008.3295, 6…
## $ X1834 <dbl> 495.1098, 432.8121, 517.3022, 1676.9184, 1296.8932, 2023.5012, 7…
## $ X1835 <dbl> 496.2316, 434.6230, 518.7858, 1696.3775, 1301.4718, 2038.7875, 7…
## $ X1836 <dbl> 497.3559, 436.4340, 520.2736, 1716.9813, 1306.0504, 2054.1893, 7…
## $ X1837 <dbl> 498.4828, 438.2449, 521.7656, 1737.5850, 1311.7737, 2069.7075, 7…
## $ X1838 <dbl> 499.6123, 440.6595, 523.2620, 1758.1888, 1316.3523, 2085.3428, 7…
## $ X1839 <dbl> 500.7443, 442.4704, 524.7626, 1778.7926, 1320.9309, 2101.0963, 7…
## $ X1840 <dbl> 501.8789, 444.2813, 526.2676, 1800.5410, 1325.5095, 2116.9688, 7…
## $ X1841 <dbl> 503.0161, 446.0922, 527.7768, 1821.1448, 1331.2328, 2132.9612, 7…
## $ X1842 <dbl> 504.1558, 448.5068, 529.2904, 1842.8932, 1335.8114, 2149.0745, 7…
## $ X1843 <dbl> 505.2982, 450.3177, 530.8083, 1864.6417, 1341.5347, 2165.3094, 7…
## $ X1844 <dbl> 506.4431, 452.1287, 532.3306, 1887.5348, 1346.1133, 2181.6670, 7…
## $ X1845 <dbl> 507.5906, 453.9396, 533.8573, 1909.2832, 1350.6919, 2198.1481, 7…
## $ X1846 <dbl> 508.7407, 456.3542, 535.3883, 1932.1763, 1356.4152, 2214.7538, 7…
## $ X1847 <dbl> 509.8934, 458.1651, 536.9237, 1955.0693, 1360.9938, 2231.4849, 7…
## $ X1848 <dbl> 511.0487, 459.9760, 538.4635, 1977.9624, 1366.7171, 2248.3424, 7…
## $ X1849 <dbl> 512.2066, 462.3906, 540.0078, 2002.0002, 1371.2957, 2265.3273, 7…
## $ X1850 <dbl> 513.3672, 464.2015, 541.5564, 2026.0379, 1377.0190, 2282.4404, 8…
## $ X1851 <dbl> 514.5304, 466.6161, 543.1095, 2050.0756, 1381.5976, 2300.7653, 8…
## $ X1852 <dbl> 515.6962, 468.4271, 544.6671, 2074.1134, 1387.3209, 2319.2372, 8…
## $ X1853 <dbl> 516.8647, 470.2380, 546.2291, 2099.2958, 1391.8995, 2337.8575, 8…
## $ X1854 <dbl> 518.0358, 472.6526, 547.7956, 2123.3335, 1397.6228, 2356.6272, 8…
## $ X1855 <dbl> 519.2096, 474.4635, 549.3666, 2148.5159, 1402.2014, 2375.5477, 8…
## $ X1856 <dbl> 520.3860, 476.8780, 550.9421, 2174.8430, 1407.9246, 2394.6200, 8…
## $ X1857 <dbl> 521.5651, 478.6890, 552.5222, 2200.0253, 1412.5033, 2413.8455, 8…
## $ X1858 <dbl> 522.7468, 481.1035, 554.1067, 2226.3524, 1418.2265, 2433.2253, 8…
## $ X1859 <dbl> 523.9313, 482.9145, 555.6958, 2252.6794, 1423.9498, 2452.7608, 8…
## $ X1860 <dbl> 525.1184, 485.3290, 557.2895, 2280.1511, 1428.5284, 2472.4530, 8…
## $ X1861 <dbl> 526.3082, 487.1400, 558.8877, 2306.4782, 1434.2517, 2492.3226, 8…
## $ X1862 <dbl> 527.5008, 489.5546, 560.4905, 2333.9499, 1439.9750, 2512.3519, 8…
## $ X1863 <dbl> 528.6960, 491.3655, 562.0979, 2362.5662, 1444.5536, 2532.5422, 8…
## $ X1864 <dbl> 529.8939, 493.7801, 563.7099, 2390.0379, 1450.2768, 2552.8947, 9…
## $ X1865 <dbl> 531.0945, 496.1946, 565.3266, 2418.6543, 1456.0001, 2573.4107, 9…
## $ X1866 <dbl> 532.2979, 498.0056, 566.9478, 2447.2706, 1461.7234, 2594.0916, 9…
## $ X1867 <dbl> 533.5040, 500.4201, 568.5738, 2477.0316, 1466.3020, 2614.9388, 9…
## $ X1868 <dbl> 534.7128, 502.2311, 570.2043, 2505.6480, 1472.0253, 2635.9534, 9…
## $ X1869 <dbl> 535.9243, 504.6456, 571.8396, 2535.4090, 1477.7486, 2657.1370, 9…
## $ X1870 <dbl> 537.1386, 507.0602, 573.4796, 2566.3147, 1483.4718, 2678.4908, 9…
## $ X1871 <dbl> 538.3557, 509.4748, 575.1242, 2596.0757, 1505.2203, 2736.7925, 9…
## $ X1872 <dbl> 539.5755, 511.2857, 576.7736, 2626.9814, 1526.9687, 2796.3632, 9…
## $ X1873 <dbl> 540.7981, 513.7003, 578.4277, 2657.8870, 1548.7171, 2857.2306, 9…
## $ X1874 <dbl> 542.0234, 516.1149, 580.0865, 2689.9373, 1571.6102, 2919.4228, 9…
## $ X1875 <dbl> 543.2515, 517.9258, 581.7501, 2721.9877, 1594.5033, 2982.9688, 1…
## $ X1876 <dbl> 544.4824, 520.3403, 583.4185, 2754.0380, 1618.5410, 2988.6921, 1…
## $ X1877 <dbl> 545.7161, 522.7549, 585.0917, 2787.2329, 1641.4341, 3227.9248, 1…
## $ X1878 <dbl> 546.9526, 525.1695, 586.7696, 2820.4279, 1665.4718, 2997.8493, 1…
## $ X1879 <dbl> 548.1919, 527.5841, 588.4524, 2853.6229, 1689.5096, 3049.3587, 1…
## $ X1880 <dbl> 549.4340, 529.9986, 590.1400, 2887.9625, 1714.6920, 2926.8807, 1…
## $ X1881 <dbl> 549.4340, 531.8096, 591.8324, 2922.3021, 1739.8744, 2908.5663, 1…
## $ X1882 <dbl> 556.3763, 534.2242, 593.5297, 2956.6417, 1765.0567, 3557.5852, 1…
## $ X1883 <dbl> 563.4063, 536.6387, 595.2319, 2992.1260, 1790.2391, 3860.9186, 1…
## $ X1884 <dbl> 570.5252, 539.0533, 596.9389, 3027.6103, 1816.5662, 3990.2645, 1…
## $ X1885 <dbl> 577.7340, 541.4679, 598.6509, 3063.0946, 1842.8932, 4468.7299, 1…
## $ X1886 <dbl> 585.0339, 543.8824, 600.3677, 3099.7235, 1870.3649, 4316.4909, 1…
## $ X1887 <dbl> 592.4260, 546.2970, 602.0895, 3136.3525, 1897.8366, 4396.6167, 1…
## $ X1888 <dbl> 599.9115, 548.7116, 603.8162, 3174.1260, 1925.3083, 4829.2960, 1…
## $ X1889 <dbl> 607.4916, 551.1262, 605.5478, 3211.8996, 1952.7800, 4908.2771, 1…
## $ X1890 <dbl> 615.1675, 553.5407, 607.2845, 3249.6732, 1981.3964, 4408.0633, 1…
## $ X1891 <dbl> 622.9404, 555.9553, 609.0261, 3288.5915, 2010.0127, 4156.2393, 1…
## $ X1892 <dbl> 630.8115, 558.3699, 610.7727, 3327.5097, 2039.7738, 4865.9249, 1…
## $ X1893 <dbl> 638.7820, 560.7844, 612.5243, 3367.5726, 2069.5348, 5046.7803, 1…
## $ X1894 <dbl> 646.8533, 563.1990, 614.2809, 3407.6355, 2099.2958, 5686.6420, 1…
## $ X1895 <dbl> 655.0265, 565.6136, 616.0426, 3447.6984, 2130.2014, 6162.8181, 1…
## $ X1896 <dbl> 663.3030, 568.0282, 617.8093, 3488.9059, 2161.1071, 6542.8433, 1…
## $ X1897 <dbl> 671.6841, 571.0464, 619.5811, 3530.1135, 2193.1574, 5150.9438, 1…
## $ X1898 <dbl> 680.1710, 573.4610, 621.3579, 3572.4657, 2225.2077, 5426.8055, 1…
## $ X1899 <dbl> 688.7653, 575.8755, 623.1399, 3614.8179, 2257.2580, 6194.8684, 1…
## $ X1900 <dbl> 697.4681, 578.2901, 624.9270, 3657.1701, 2290.4530, 5245.9501, 1…
## $ X1901 <dbl> 706.2808, 580.7047, 626.7192, 3700.6670, 2323.6480, 5255.1074, 1…
## $ X1902 <dbl> 715.2050, 583.7229, 628.5165, 3745.3085, 2357.9876, 4957.4973, 1…
## $ X1903 <dbl> 724.2418, 586.1374, 630.3190, 3788.8053, 2392.3272, 5458.8558, 1…
## $ X1904 <dbl> 733.3929, 588.5520, 632.1267, 3834.5915, 2426.6669, 5821.7112, 1…
## $ X1905 <dbl> 742.6596, 590.9666, 633.9395, 3880.3777, 2462.1512, 6347.1074, 1…
## $ X1906 <dbl> 752.0434, 593.9848, 635.7576, 3926.1638, 2497.6354, 6419.2207, 1…
## $ X1907 <dbl> 761.5457, 596.3994, 637.5809, 3973.0947, 2534.2644, 6311.6232, 1…
## $ X1908 <dbl> 771.1681, 598.8140, 639.4093, 4020.0255, 2572.0379, 6672.1892, 1…
## $ X1909 <dbl> 780.9121, 601.8322, 641.2431, 4068.1010, 2608.6669, 6748.8811, 1…
## $ X1910 <dbl> 790.7793, 604.2467, 643.0821, 4116.1764, 2647.5851, 6973.2333, 1…
## $ X1911 <dbl> 800.7710, 607.2650, 644.9263, 4165.3966, 2685.3587, 6834.7301, 1…
## $ X1912 <dbl> 810.8891, 609.6795, 646.7759, 4214.6167, 2724.2769, 7123.1830, 1…
## $ X1913 <dbl> 821.1350, 612.0941, 648.6308, 4264.9815, 2764.3398, 6927.4471, 1…
## $ X1914 <dbl> 831.5103, 630.8070, 667.9330, 4335.9500, 2630.4153, 6024.3150, 1…
## $ X1915 <dbl> 842.0168, 649.5200, 687.8098, 4409.2079, 2503.3587, 5919.0068, 1…
## $ X1916 <dbl> 852.6560, 668.8366, 708.2780, 4482.4658, 2383.1700, 5639.7112, 1…
## $ X1917 <dbl> 863.4296, 688.7568, 729.3553, 4558.0130, 2267.5599, 5090.2771, 1…
## $ X1918 <dbl> 874.3393, 708.6770, 751.0598, 4634.7048, 2157.6731, 5925.8747, 1…
## $ X1919 <dbl> 885.3869, 729.8045, 773.4103, 4712.5413, 2273.2832, 6033.4722, 1…
## $ X1920 <dbl> 896.5742, 751.5357, 796.4258, 4791.5224, 2394.6166, 6336.8056, 1…
## $ X1921 <dbl> 907.9027, 775.6814, 820.1263, 4873.9375, 2522.8178, 6333.3716, 9…
## $ X1922 <dbl> 919.3744, 800.4307, 844.5321, 4958.6419, 2656.7424, 6634.4156, 1…
## $ X1923 <dbl> 930.9910, 825.7838, 869.6641, 5044.4910, 2638.4279, 7111.7364, 1…
## $ X1924 <dbl> 942.7545, 852.3441, 895.5440, 5131.4847, 2620.1134, 7399.0446, 1…
## $ X1925 <dbl> 954.6665, 879.5080, 922.1941, 5219.6231, 2601.7990, 7150.6547, 1…
## $ X1926 <dbl> 966.7291, 907.2756, 949.6372, 5310.0508, 2544.5662, 7286.8685, 1…
## $ X1927 <dbl> 978.9440, 936.2505, 977.8971, 5401.6231, 2489.6229, 7583.3340, 1…
## $ X1928 <dbl> 991.3134, 965.8290, 1006.9978, 5494.3401, 2435.8241, 7829.4346, …
## $ X1929 <dbl> 1003.8390, 996.6148, 1036.9646, 5589.3464, 2258.4027, 7967.9378,…
## $ X1930 <dbl> 1016.5229, 1028.6078, 1067.8232, 5685.4973, 2094.7171, 7443.6862…
## $ X1931 <dbl> 1029.3670, 1061.2046, 1099.6000, 5782.7929, 1942.4781, 6772.9188…
## $ X1932 <dbl> 1042.3735, 1094.4049, 1132.3225, 5882.3778, 1801.6857, 6426.0886…
## $ X1933 <dbl> 1055.5442, 1129.4162, 1166.0188, 5984.2521, 1671.1951, 6606.9439…
## $ X1934 <dbl> 1068.8814, 1165.0312, 1200.7178, 6087.2709, 1549.8618, 7015.5855…
## $ X1935 <dbl> 1082.3871, 1202.4571, 1236.4494, 6192.5791, 1525.8240, 7206.7427…
## $ X1936 <dbl> 1096.0635, 1240.4866, 1273.2443, 6299.0320, 1502.9309, 7138.0635…
## $ X1937 <dbl> 1109.9127, 1279.7234, 1311.1342, 6406.6295, 1480.0379, 7526.1012…
## $ X1938 <dbl> 1123.9368, 1320.1675, 1350.1517, 6516.5163, 1457.1448, 7429.9503…
## $ X1939 <dbl> 1138.1382, 1362.4225, 1390.3302, 6628.6924, 1434.2517, 7568.4534…
## $ X1940 <dbl> 1152.5190, 1405.2811, 1431.7044, 6743.1578, 1412.5033, 7592.4912…
## $ X1941 <dbl> 1179.1682, 1472.1728, 1478.5126, 6888.6091, 1420.7092, 7883.1469…
## $ X1942 <dbl> 1206.4336, 1542.0294, 1526.8511, 7037.5077, 1429.8078, 7876.0272…
## $ X1943 <dbl> 1234.3294, 1615.5769, 1576.7700, 7189.8909, 1437.4191, 7717.0854…
## $ X1944 <dbl> 1262.8703, 1692.2995, 1628.3210, 7344.6316, 1445.9415, 8481.8240…
## $ X1945 <dbl> 1292.0711, 1772.2972, 1681.5574, 7502.9222, 1487.2712, 8098.6188…
## $ X1946 <dbl> 1321.9471, 1856.3333, 1736.5342, 7664.8009, 1529.7106, 8706.4492…
## $ X1947 <dbl> 1352.5139, 1944.5435, 1793.3085, 7830.3061, 1573.2881, 9533.8328…
## $ X1948 <dbl> 1383.7875, 2037.0667, 1851.9390, 7999.4766, 1618.0331, 9876.5827…
## $ X1949 <dbl> 1415.7842, 2133.3535, 1912.4864, 8172.3515, 1663.9758, 9526.6768…
## $ X1950 <dbl> 1448.5207, 2234.9246, 1975.0132, 8348.9701, 1711.1469, 9448.5634…
## $ X1951 <dbl> 1499.9625, 2320.5957, 1967.6516, 9064.8592, 1758.1311, 9676.7585…
## $ X1952 <dbl> 1540.1072, 2411.1156, 1974.4452, 9843.4955, 1804.8672, 9020.7066…
## $ X1953 <dbl> 1622.8013, 2503.8650, 2062.0323, 10688.0801, 1854.3109, 9338.148…
## $ X1954 <dbl> 1646.2189, 2436.0178, 2126.2789, 11605.5124, 1905.0473, 9560.983…
## $ X1955 <dbl> 1664.6037, 2631.5320, 2248.2153, 12601.5827, 1957.1090, 10088.51…
## $ X1956 <dbl> 1725.0363, 2582.8010, 2278.5840, 13683.4012, 2008.9198, 10214.85…
## $ X1957 <dbl> 1708.8070, 2828.4404, 2430.4500, 14856.9607, 2063.6990, 10561.48…
## $ X1958 <dbl> 1788.5093, 2977.1485, 2547.0182, 16132.0623, 2363.4767, 11070.58…
## $ X1959 <dbl> 1819.5612, 2984.5717, 2659.3983, 17516.1811, 2706.0981, 10265.97…
## $ X1960 <dbl> 1865.1303, 3098.5848, 2802.6911, 19019.4082, 3097.4406, 11017.09…
## $ X1961 <dbl> 1860.2866, 3505.2652, 2833.7688, 20650.7446, 3533.0366, 11536.45…
## $ X1962 <dbl> 1869.5013, 3403.8314, 2935.6815, 22423.1128, 4029.8618, 11292.77…
## $ X1963 <dbl> 1883.0595, 3572.9312, 3044.2627, 24345.8517, 5349.9733, 10847.22…
## $ X1964 <dbl> 1892.3007, 3969.0230, 3157.1159, 26434.7988, 9298.2882, 11734.27…
## $ X1965 <dbl> 1910.4171, 4259.2211, 3281.6494, 28702.2234, 16155.9310, 12660.5…
## $ X1966 <dbl> 1903.2689, 4497.8862, 3414.2629, 31164.4340, 28071.5711, 12584.1…
## $ X1967 <dbl> 1930.5387, 4751.4962, 3555.0224, 33836.7168, 48773.1144, 12787.1…
## $ X1968 <dbl> 1970.3887, 4670.1082, 3692.8377, 36738.4800, 59932.5844, 13234.6…
## $ X1969 <dbl> 1972.2756, 4795.0600, 3828.9236, 39889.4467, 73641.8131, 14241.8…
## $ X1970 <dbl> 1983.3458, 5090.7505, 3981.9625, 43309.6581, 84894.1671, 14699.6…
## $ X1971 <dbl> 1948.7476, 5178.2863, 4109.9462, 43257.1329, 85963.4879, 15144.4…
## $ X1972 <dbl> 1602.8391, 5160.2660, 4237.3900, 44677.7260, 86134.5584, 15337.8…
## $ X1973 <dbl> 1625.8713, 5464.7349, 4391.2791, 46091.3682, 86371.3252, 15881.6…
## $ X1974 <dbl> 1688.2048, 5399.6289, 4442.8963, 46785.4430, 92799.1153, 16595.3…
## $ X1975 <dbl> 1763.1467, 4155.3419, 4498.0607, 45427.3173, 84992.9455, 16248.0…
## $ X1976 <dbl> 1832.8250, 3831.1875, 4558.4149, 45556.7106, 86190.3712, 16009.8…
## $ X1977 <dbl> 1712.711, 3836.637, 4617.278, 45651.423, 89667.239, 16782.176, 7…
## $ X1978 <dbl> 1811.722, 3772.016, 4690.086, 45191.263, 78291.711, 15943.062, 7…
## $ X1979 <dbl> 1750.081, 3780.290, 4761.107, 44025.270, 88474.524, 16854.544, 6…
## $ X1980 <dbl> 1761.291, 3837.396, 4818.871, 43650.335, 102507.988, 16876.786, …
## $ X1981 <dbl> 1997.826, 3689.767, 4945.846, 42117.002, 102150.342, 15681.658, …
## $ X1982 <dbl> 2240.835, 3602.920, 4995.939, 41080.481, 90360.120, 14981.304, 7…
## $ X1983 <dbl> 2401.310, 3618.094, 4992.611, 40205.213, 83505.668, 15349.644, 7…
## $ X1984 <dbl> 2408.167, 3738.151, 5014.838, 39342.334, 82608.528, 15454.406, 7…
## $ X1985 <dbl> 2373.053, 3599.300, 4925.418, 38727.244, 77696.570, 14211.585, 7…
## $ X1986 <dbl> 2471.176, 3532.103, 5063.914, 38519.766, 60374.516, 15072.944, 7…
## $ X1987 <dbl> 2249.692, 3758.138, 4994.201, 39209.553, 61554.765, 15307.349, 6…
## $ X1988 <dbl> 2066.526, 4071.767, 4904.650, 39780.546, 53561.922, 14860.832, 6…
## $ X1989 <dbl> 1894.496, 4077.400, 5159.301, 40283.550, 59513.725, 13717.075, 7…
## $ X1990 <dbl> 1845.138, 3999.671, 4748.927, 40413.308, 72062.074, 13372.717, 6…
## $ X1991 <dbl> 1705.506, 4046.648, 3460.931, 40034.943, 72004.827, 14492.909, 5…
## $ X1992 <dbl> 1650.799, 3811.346, 3261.805, 39045.704, 73233.770, 15569.354, 3…
## $ X1993 <dbl> 1142.232, 2892.518, 3596.858, 37476.544, 73370.674, 16600.890, 2…
## $ X1994 <dbl> 854.5642, 2922.0132, 3914.7856, 37489.1029, 77636.7456, 17393.65…
## $ X1995 <dbl> 1276.9657, 3341.2075, 4411.3853, 37995.2043, 81887.6857, 16732.7…
## $ X1996 <dbl> 1222.9292, 3765.7755, 4832.7579, 39674.2066, 84634.2940, 17491.6…
## $ X1997 <dbl> 1175.8739, 4003.8450, 4330.4909, 43583.7441, 88626.0696, 18738.3…
## $ X1998 <dbl> 1133.9547, 4149.6461, 4750.6196, 45378.2994, 86027.6322, 19287.3…
## $ X1999 <dbl> 1091.0412, 4193.0997, 5409.6738, 47233.4885, 85689.6813, 18468.7…
## $ X2000 <dbl> 1066.2329, 4267.2055, 5835.3582, 47026.0924, 91954.6163, 18160.2…
## $ X2001 <dbl> 978.7510, 4382.4705, 6389.8144, 47427.6007, 90147.2925, 17209.00…
## $ X2002 <dbl> 1232.3349, 4896.6003, 6714.9047, 47741.8204, 89212.9600, 15203.1…
## $ X2003 <dbl> 1255.6795, 4953.4115, 7125.4537, 51912.9795, 93640.9013, 16409.0…
## $ X2004 <dbl> 1235.3309, 5370.3789, 7560.0073, 54904.4690, 98774.2794, 17741.5…
## $ X2005 <dbl> 1333.4234, 6038.6918, 8026.3556, 60390.5422, 97266.3650, 19145.6…
## $ X2006 <dbl> 1358.1176, 6586.3552, 8559.3966, 65151.9423, 94130.6070, 20506.4…
## $ X2007 <dbl> 1525.2672, 7307.4551, 9143.7998, 66513.2887, 81657.8897, 22162.7…
## $ X2008 <dbl> 1556.2543, 7861.9563, 9907.6161, 63702.5839, 71139.9628, 22864.2…
## $ X2009 <dbl> 1824.023, 7652.625, 10311.404, 64456.972, 59008.924, 21319.016, …
## $ X2010 <dbl> 2027.1567, 7689.8207, 10749.4664, 60467.0860, 56388.8134, 23442.…
## $ X2011 <dbl> 1962.057, 7663.286, 11052.778, 60911.089, 59241.416, 24564.784, …
## $ X2012 <dbl> 2123.8710, 8011.0501, 11227.9504, 57892.7180, 59697.3605, 24037.…
## $ X2013 <dbl> 2166.4020, 8099.6788, 11361.2525, 55672.9160, 62092.8318, 24342.…
## $ X2014 <dbl> 2145.5005, 8183.1646, 11586.8175, 56578.7930, 64063.7840, 23470.…
## $ X2015 <dbl> 2109.7475, 7966.8856, 11878.4376, 56460.5380, 67790.6036, 23853.…
## $ X2016 <dbl> 2102.4519, 7487.9251, 12291.8421, 57455.3080, 70945.2430, 23111.…
## $ X2017 <dbl> 2097.1202, 7216.0614, 12770.9919, 56352.1970, 70883.2893, 23517.…
## $ X2018 <dbl> 2061.7087, 6878.5900, 13317.1193, 56208.1680, 71249.9260, 22670.…
## $ X2019 <dbl> 2080.9411, 6602.2692, 13653.1822, 56330.9480, 71480.5524, 21997.…
## $ X2020 <dbl> 1969.3055, 6029.6919, 13278.3698, 49728.2360, 67383.9701, 19628.…
## $ X2021 <dbl> 1517.0163, 5911.8357, 14595.9444, 52819.9900, 69733.7938, 21527.…
## $ X2022 <dbl> 1386.7554, 5906.1157, 15491.9919, 56415.7830, 74602.8984, 22385.…
## $ X2023 <dbl> 1359.0203, 5778.8343, 16209.8769, 55317.3960, 76528.3216, 21840.…
## $ X2024 <dbl> 1331.8399, 5754.0385, 16760.5702, 55218.6400, 78598.8995, 21026.…
## $ X2025 <dbl> 1331.8399, 5761.2020, 17385.4003, 54957.6230, 81255.0037, 21859.…
## $ X2026 <dbl> 1345.1582, 5783.2739, 18056.1788, 54697.8400, 84098.4282, 22616.…
## $ X2027 <dbl> 1358.6098, 5842.1228, 18721.2726, 54503.9245, 86325.9760, 23208.…
## $ X2028 <dbl> 1385.7820, 5928.3042, 19379.1450, 54375.1055, 87720.1910, 23820.…
## $ X2029 <dbl> 1420.4266, 6049.6914, 20031.6444, 54310.8481, 88310.1349, 24355.…
## $ X2030 <dbl> 1415.5754, 5932.4808, 19367.4691, 51977.6540, 88033.0006, 24043.…
## $ X2031 <dbl> 1441.0557, 6074.2358, 19801.3983, 52578.0857, 88165.5657, 24542.…
## $ X2032 <dbl> 1466.9948, 6219.7766, 20242.4069, 53175.7438, 88295.3808, 25048.…
## $ X2033 <dbl> 1493.4007, 6369.1924, 20690.4638, 53770.4548, 88422.4963, 25559.…
## $ X2034 <dbl> 1520.2819, 6522.5729, 21145.5315, 54362.0499, 88546.9618, 26077.…
## $ X2035 <dbl> 1547.6470, 6680.0094, 21607.5655, 54950.3650, 88668.8264, 26600.…
## $ X2036 <dbl> 1575.5046, 6841.5935, 22076.5145, 55535.2407, 88788.1387, 27130.…
## $ X2037 <dbl> 1603.8637, 7007.4179, 22552.3201, 56116.5227, 88904.9465, 27664.…
## $ X2038 <dbl> 1632.7332, 7177.5757, 23034.9172, 56694.0617, 89019.2969, 28205.…
## $ X2039 <dbl> 1662.1224, 7352.1610, 23524.2332, 57267.7133, 89131.2364, 28751.…
## $ X2040 <dbl> 1692.041, 7531.268, 24020.189, 57837.338, 89240.811, 29302.267, …
## $ X2041 <dbl> 1722.4974, 7714.9920, 24522.6982, 58402.8034, 89348.0659, 29858.…
## $ X2042 <dbl> 1753.5023, 7903.4277, 25031.6672, 58963.9795, 89453.0456, 30419.…
## $ X2043 <dbl> 1785.0654, 8096.6705, 25546.9958, 59520.7435, 89555.7941, 30985.…
## $ X2044 <dbl> 1817.1965, 8294.8160, 26068.5765, 60072.9775, 89656.3546, 31555.…
## $ X2045 <dbl> 1849.906, 8497.960, 26596.295, 60620.569, 89754.770, 32130.040, …
## $ X2046 <dbl> 1883.204, 8706.196, 27130.031, 61163.411, 89851.081, 32708.915, …
## $ X2047 <dbl> 1917.102, 8919.622, 27669.655, 61701.401, 89945.331, 33291.804, …
## $ X2048 <dbl> 1951.610, 9138.330, 28215.035, 62234.443, 90037.559, 33878.516, …
## $ X2049 <dbl> 1986.214, 9362.414, 28766.028, 62762.446, 90127.806, 34468.855, …
## $ X2050 <dbl> 2021.714, 9591.969, 29322.489, 63285.324, 90216.111, 35062.623, …
## $ X2051 <dbl> 2058.137, 9827.087, 29884.262, 63802.996, 90302.512, 35659.616, …
## $ X2052 <dbl> 2095.510, 10067.858, 30451.191, 64315.387, 90387.048, 36259.629,…
## $ X2053 <dbl> 2133.860, 10314.372, 31023.108, 64822.427, 90469.757, 36862.453,…
## $ X2054 <dbl> 2173.215, 10566.720, 31599.844, 65324.050, 90550.674, 37467.876,…
## $ X2055 <dbl> 2213.604, 10824.987, 32181.222, 65820.197, 90629.837, 38075.684,…
## $ X2056 <dbl> 2255.058, 11089.258, 32767.062, 66310.812, 90707.281, 38685.661,…
## $ X2057 <dbl> 2297.608, 11359.619, 33357.176, 66795.846, 90783.041, 39297.591,…
## $ X2058 <dbl> 2341.284, 11636.148, 33951.375, 67275.251, 90857.151, 39911.253,…
## $ X2059 <dbl> 2386.121, 11918.927, 34549.463, 67748.989, 90929.645, 40526.429,…
## $ X2060 <dbl> 2432.151, 12208.031, 35151.240, 68217.022, 91000.556, 41142.897,…
## $ X2061 <dbl> 2479.409, 12503.533, 35756.504, 68679.319, 91069.917, 41760.436,…
## $ X2062 <dbl> 2527.931, 12805.505, 36365.047, 69135.853, 91137.760, 42378.826,…
## $ X2063 <dbl> 2577.753, 13114.015, 36976.660, 69586.601, 91204.116, 42997.844,…
## $ X2064 <dbl> 2628.913, 13429.126, 37591.130, 70031.543, 91269.016, 43617.271,…
## $ X2065 <dbl> 2681.450, 13750.899, 38208.240, 70470.666, 91332.491, 44236.887,…
## $ X2066 <dbl> 2735.402, 14079.392, 38827.774, 70903.959, 91394.570, 44856.473,…
## $ X2067 <dbl> 2790.812, 14414.658, 39449.510, 71331.414, 91455.282, 45475.812,…
## $ X2068 <dbl> 2847.722, 14756.745, 40073.227, 71753.029, 91514.656, 46094.688,…
## $ X2069 <dbl> 2906.173, 15105.698, 40698.702, 72168.805, 91572.721, 46712.888,…
## $ X2070 <dbl> 2966.210, 15461.557, 41325.711, 72578.745, 91629.503, 47330.200,…
## $ X2071 <dbl> 3027.879, 15824.357, 41954.029, 72982.857, 91685.030, 47946.415,…
## $ X2072 <dbl> 3091.227, 16194.128, 42583.430, 73381.151, 91739.328, 48561.328,…
## $ X2073 <dbl> 3156.300, 16570.897, 43213.689, 73773.642, 91792.424, 49174.734,…
## $ X2074 <dbl> 3223.149, 16954.681, 43844.581, 74160.346, 91844.342, 49786.434,…
## $ X2075 <dbl> 3291.823, 17345.497, 44475.881, 74541.284, 91895.109, 50396.232,…
## $ X2076 <dbl> 3362.374, 17743.351, 45107.366, 74916.477, 91944.748, 51003.933,…
## $ X2077 <dbl> 3434.854, 18148.248, 45738.811, 75285.952, 91993.284, 51609.349,…
## $ X2078 <dbl> 3509.319, 18560.184, 46369.997, 75649.736, 92040.740, 52212.294,…
## $ X2079 <dbl> 3585.823, 18979.149, 47000.703, 76007.859, 92087.139, 52812.588,…
## $ X2080 <dbl> 3664.423, 19405.128, 47630.712, 76360.355, 92132.505, 53410.054,…
## $ X2081 <dbl> 3745.178, 19838.100, 48259.808, 76707.257, 92176.858, 54004.519,…
## $ X2082 <dbl> 3828.147, 20278.034, 48887.779, 77048.604, 92220.222, 54595.815,…
## $ X2083 <dbl> 3913.390, 20724.897, 49514.414, 77384.434, 92262.617, 55183.781,…
## $ X2084 <dbl> 4000.970, 21178.647, 50139.506, 77714.788, 92304.065, 55768.257,…
## $ X2085 <dbl> 4090.950, 21639.234, 50762.852, 78039.708, 92344.586, 56349.092,…
## $ X2086 <dbl> 4183.396, 22106.604, 51384.251, 78359.240, 92384.199, 56926.136,…
## $ X2087 <dbl> 4278.374, 22580.694, 52003.508, 78673.430, 92422.926, 57499.246,…
## $ X2088 <dbl> 4375.951, 23061.435, 52620.427, 78982.324, 92460.784, 58068.286,…
## $ X2089 <dbl> 4476.197, 23548.750, 53234.823, 79285.971, 92497.794, 58633.123,…
## $ X2090 <dbl> 4579.183, 24042.557, 53846.509, 79584.423, 92533.972, 59193.629,…
## $ X2091 <dbl> 4684.979, 24542.766, 54455.306, 79877.729, 92569.339, 59749.683,…
## $ X2092 <dbl> 4793.659, 25049.279, 55061.039, 80165.944, 92603.910, 60301.168,…
## $ X2093 <dbl> 4905.298, 25561.993, 55663.536, 80449.120, 92637.705, 60847.973,…
## $ X2094 <dbl> 5019.972, 26080.797, 56262.633, 80727.312, 92670.739, 61389.992,…
## $ X2095 <dbl> 5137.757, 26605.573, 56858.168, 81000.575, 92703.029, 61927.126,…
## $ X2096 <dbl> 5258.733, 27136.198, 57449.985, 81268.966, 92734.592, 62459.279,…
## $ X2097 <dbl> 5382.979, 27672.541, 58037.933, 81532.541, 92765.444, 62986.361,…
## $ X2098 <dbl> 5510.575, 28214.465, 58621.868, 81791.358, 92795.601, 63508.288,…
## $ X2099 <dbl> 5641.604, 28761.827, 59201.649, 82045.475, 92825.077, 64024.981,…
## $ X2100 <dbl> 5776.149, 29314.477, 59777.142, 82294.951, 92853.888, 64536.366,…
hdi_clean <- clean_names(hdi)
names(hdi_clean) <- gsub("^x", "", names(hdi_clean))
head(hdi_clean, 1)
## country 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
## 1 Afghanistan 0.285 0.291 0.301 0.311 0.305 0.329 0.334 0.338 0.338 0.347 0.351
## 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
## 1 0.355 0.383 0.392 0.408 0.417 0.426 0.442 0.446 0.458 0.465 0.474 0.484 0.492
## 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
## 1 0.497 0.496 0.495 0.496 0.498 0.507 0.501 0.486 0.495 0.496
#changing the columns in broadband to dbl and treat missing values as NA
broadband <- broadband %>%
mutate(across(c("X2003","X2004","X2005","X2006","X2010","X2011"),
as.numeric))
broadband_clean <- clean_names(broadband)
names(broadband_clean) <- gsub("^x", "", names(broadband_clean))
head(broadband_clean, 1)
## country 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
## 1 Aruba NA NA NA NA NA 1.52 7.47 13 14.6 16.6 18.8 19 19.2 NA
## 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
## 1 NA 18.7 18.6 18.2 NA NA NA 17.7 17.7 17.7 17.5 NA
gdp_clean <- clean_names(gdp)
names(gdp_clean) <- gsub("^X","", names(gdp))
head(gdp_clean,1)
## geo name 1800 1801 1802 1803 1804 1805
## 1 afg Afghanistan 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548
## 1806 1807 1808 1809 1810 1811 1812 1813
## 1 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548
## 1814 1815 1816 1817 1818 1819 1820 1821
## 1 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548 480.7548
## 1822 1823 1824 1825 1826 1827 1828 1829
## 1 481.844 482.9358 484.0301 485.1268 486.226 487.3277 488.4319 489.5386
## 1830 1831 1832 1833 1834 1835 1836 1837
## 1 490.6478 491.7595 492.8737 493.9905 495.1098 496.2316 497.3559 498.4828
## 1838 1839 1840 1841 1842 1843 1844 1845
## 1 499.6123 500.7443 501.8789 503.0161 504.1558 505.2982 506.4431 507.5906
## 1846 1847 1848 1849 1850 1851 1852 1853
## 1 508.7407 509.8934 511.0487 512.2066 513.3672 514.5304 515.6962 516.8647
## 1854 1855 1856 1857 1858 1859 1860 1861
## 1 518.0358 519.2096 520.386 521.5651 522.7468 523.9313 525.1184 526.3082
## 1862 1863 1864 1865 1866 1867 1868 1869
## 1 527.5008 528.696 529.8939 531.0945 532.2979 533.504 534.7128 535.9243
## 1870 1871 1872 1873 1874 1875 1876 1877
## 1 537.1386 538.3557 539.5755 540.7981 542.0234 543.2515 544.4824 545.7161
## 1878 1879 1880 1881 1882 1883 1884 1885 1886
## 1 546.9526 548.1919 549.434 549.434 556.3763 563.4063 570.5252 577.734 585.0339
## 1887 1888 1889 1890 1891 1892 1893 1894
## 1 592.426 599.9115 607.4916 615.1675 622.9404 630.8115 638.782 646.8533
## 1895 1896 1897 1898 1899 1900 1901 1902 1903
## 1 655.0265 663.303 671.6841 680.171 688.7653 697.4681 706.2808 715.205 724.2418
## 1904 1905 1906 1907 1908 1909 1910 1911
## 1 733.3929 742.6596 752.0434 761.5457 771.1681 780.9121 790.7793 800.771
## 1912 1913 1914 1915 1916 1917 1918 1919 1920
## 1 810.8891 821.135 831.5103 842.0168 852.656 863.4296 874.3393 885.387 896.5742
## 1921 1922 1923 1924 1925 1926 1927 1928
## 1 907.9027 919.3744 930.991 942.7545 954.6665 966.7291 978.944 991.3134
## 1929 1930 1931 1932 1933 1934 1935 1936
## 1 1003.839 1016.523 1029.367 1042.373 1055.544 1068.881 1082.387 1096.064
## 1937 1938 1939 1940 1941 1942 1943 1944
## 1 1109.913 1123.937 1138.138 1152.519 1179.168 1206.434 1234.329 1262.87
## 1945 1946 1947 1948 1949 1950 1951 1952
## 1 1292.071 1321.947 1352.514 1383.787 1415.784 1448.521 1499.963 1540.107
## 1953 1954 1955 1956 1957 1958 1959 1960
## 1 1622.801 1646.219 1664.604 1725.036 1708.807 1788.509 1819.561 1865.13
## 1961 1962 1963 1964 1965 1966 1967 1968
## 1 1860.287 1869.501 1883.06 1892.301 1910.417 1903.269 1930.539 1970.389
## 1969 1970 1971 1972 1973 1974 1975 1976
## 1 1972.276 1983.346 1948.748 1602.839 1625.871 1688.205 1763.147 1832.825
## 1977 1978 1979 1980 1981 1982 1983 1984
## 1 1712.711 1811.722 1750.081 1761.291 1997.826 2240.835 2401.31 2408.167
## 1985 1986 1987 1988 1989 1990 1991 1992
## 1 2373.053 2471.176 2249.692 2066.526 1894.496 1845.138 1705.506 1650.799
## 1993 1994 1995 1996 1997 1998 1999 2000
## 1 1142.232 854.5642 1276.966 1222.929 1175.874 1133.955 1091.041 1066.233
## 2001 2002 2003 2004 2005 2006 2007 2008
## 1 978.751 1232.335 1255.679 1235.331 1333.423 1358.118 1525.267 1556.254
## 2009 2010 2011 2012 2013 2014 2015 2016 2017
## 1 1824.023 2027.157 1962.057 2123.871 2166.402 2145.5 2109.747 2102.452 2097.12
## 2018 2019 2020 2021 2022 2023 2024 2025 2026
## 1 2061.709 2080.941 1969.306 1517.016 1386.755 1359.02 1331.84 1331.84 1345.158
## 2027 2028 2029 2030 2031 2032 2033 2034
## 1 1358.61 1385.782 1420.427 1415.575 1441.056 1466.995 1493.401 1520.282
## 2035 2036 2037 2038 2039 2040 2041 2042
## 1 1547.647 1575.505 1603.864 1632.733 1662.122 1692.041 1722.497 1753.502
## 2043 2044 2045 2046 2047 2048 2049 2050
## 1 1785.065 1817.197 1849.906 1883.204 1917.102 1951.61 1986.214 2021.714
## 2051 2052 2053 2054 2055 2056 2057 2058
## 1 2058.137 2095.51 2133.86 2173.215 2213.604 2255.058 2297.608 2341.284
## 2059 2060 2061 2062 2063 2064 2065 2066
## 1 2386.121 2432.151 2479.409 2527.931 2577.753 2628.913 2681.45 2735.402
## 2067 2068 2069 2070 2071 2072 2073 2074 2075
## 1 2790.812 2847.722 2906.173 2966.21 3027.879 3091.227 3156.3 3223.149 3291.823
## 2076 2077 2078 2079 2080 2081 2082 2083
## 1 3362.374 3434.854 3509.319 3585.823 3664.423 3745.178 3828.147 3913.39
## 2084 2085 2086 2087 2088 2089 2090 2091
## 1 4000.97 4090.95 4183.396 4278.374 4375.951 4476.197 4579.183 4684.979
## 2092 2093 2094 2095 2096 2097 2098 2099
## 1 4793.659 4905.298 5019.972 5137.757 5258.733 5382.979 5510.575 5641.604
## 2100
## 1 5776.149
remove_duplicates <- function(data) {
original_rows <- nrow(data)
data_noDuplicates <- data %>%
distinct()
new_rows <- nrow(data_noDuplicates)
rows_removed <- original_rows - new_rows
print_statement <- paste(rows_removed, "rows removed.")
cat(print_statement)
return(data_noDuplicates)
}
hdi_clean <- remove_duplicates(hdi_clean)
## 0 rows removed.
broadband_clean <- remove_duplicates(broadband_clean)
## 0 rows removed.
gdp_clean <- remove_duplicates(gdp_clean)
## 0 rows removed.
skim(hdi_clean)
| Name | hdi_clean |
| Number of rows | 193 |
| Number of columns | 35 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 34 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1 | 2 | 30 | 0 | 193 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| 1990 | 52 | 0.73 | 0.61 | 0.17 | 0.22 | 0.49 | 0.64 | 0.74 | 0.88 | ▂▃▅▇▅ |
| 1991 | 52 | 0.73 | 0.61 | 0.17 | 0.22 | 0.49 | 0.64 | 0.74 | 0.88 | ▂▃▅▇▅ |
| 1992 | 52 | 0.73 | 0.61 | 0.17 | 0.22 | 0.49 | 0.64 | 0.74 | 0.88 | ▂▃▅▇▅ |
| 1993 | 51 | 0.74 | 0.62 | 0.17 | 0.23 | 0.49 | 0.64 | 0.74 | 0.88 | ▂▅▅▇▆ |
| 1994 | 51 | 0.74 | 0.62 | 0.17 | 0.23 | 0.49 | 0.65 | 0.75 | 0.89 | ▂▅▅▇▆ |
| 1995 | 44 | 0.77 | 0.62 | 0.17 | 0.24 | 0.49 | 0.65 | 0.75 | 0.89 | ▂▅▅▇▆ |
| 1996 | 44 | 0.77 | 0.63 | 0.17 | 0.24 | 0.50 | 0.66 | 0.75 | 0.89 | ▂▅▅▇▆ |
| 1997 | 44 | 0.77 | 0.64 | 0.17 | 0.25 | 0.50 | 0.66 | 0.76 | 0.90 | ▂▅▅▇▆ |
| 1998 | 44 | 0.77 | 0.64 | 0.17 | 0.26 | 0.51 | 0.67 | 0.77 | 0.91 | ▂▅▅▇▆ |
| 1999 | 40 | 0.79 | 0.64 | 0.17 | 0.26 | 0.50 | 0.67 | 0.77 | 0.91 | ▂▅▅▇▆ |
| 2000 | 22 | 0.89 | 0.65 | 0.17 | 0.27 | 0.50 | 0.67 | 0.78 | 0.92 | ▂▅▅▇▅ |
| 2001 | 22 | 0.89 | 0.65 | 0.17 | 0.27 | 0.51 | 0.68 | 0.79 | 0.92 | ▂▅▅▇▆ |
| 2002 | 21 | 0.89 | 0.66 | 0.17 | 0.28 | 0.51 | 0.68 | 0.79 | 0.92 | ▂▅▃▇▆ |
| 2003 | 20 | 0.90 | 0.66 | 0.17 | 0.28 | 0.53 | 0.69 | 0.80 | 0.93 | ▂▅▅▇▆ |
| 2004 | 18 | 0.91 | 0.67 | 0.16 | 0.29 | 0.54 | 0.70 | 0.81 | 0.94 | ▂▅▅▇▆ |
| 2005 | 7 | 0.96 | 0.67 | 0.16 | 0.30 | 0.54 | 0.70 | 0.80 | 0.94 | ▂▅▅▇▆ |
| 2006 | 7 | 0.96 | 0.68 | 0.16 | 0.31 | 0.55 | 0.70 | 0.80 | 0.94 | ▂▅▅▇▆ |
| 2007 | 6 | 0.97 | 0.69 | 0.16 | 0.31 | 0.55 | 0.71 | 0.81 | 0.94 | ▂▅▅▇▆ |
| 2008 | 5 | 0.97 | 0.69 | 0.16 | 0.32 | 0.56 | 0.72 | 0.82 | 0.94 | ▂▅▅▇▆ |
| 2009 | 6 | 0.97 | 0.70 | 0.16 | 0.33 | 0.56 | 0.72 | 0.82 | 0.94 | ▂▅▅▇▆ |
| 2010 | 2 | 0.99 | 0.70 | 0.16 | 0.34 | 0.56 | 0.72 | 0.82 | 0.95 | ▂▅▅▇▆ |
| 2011 | 1 | 0.99 | 0.70 | 0.16 | 0.35 | 0.57 | 0.72 | 0.82 | 0.95 | ▂▅▅▇▆ |
| 2012 | 1 | 0.99 | 0.71 | 0.15 | 0.36 | 0.58 | 0.74 | 0.83 | 0.95 | ▂▅▅▇▇ |
| 2013 | 1 | 0.99 | 0.71 | 0.15 | 0.36 | 0.58 | 0.74 | 0.83 | 0.95 | ▂▅▅▇▇ |
| 2014 | 1 | 0.99 | 0.72 | 0.15 | 0.32 | 0.60 | 0.74 | 0.84 | 0.96 | ▁▅▅▇▇ |
| 2015 | 1 | 0.99 | 0.72 | 0.15 | 0.32 | 0.60 | 0.74 | 0.84 | 0.96 | ▁▅▅▇▇ |
| 2016 | 1 | 0.99 | 0.72 | 0.15 | 0.30 | 0.61 | 0.75 | 0.84 | 0.96 | ▁▃▅▇▇ |
| 2017 | 1 | 0.99 | 0.73 | 0.15 | 0.29 | 0.61 | 0.75 | 0.85 | 0.96 | ▁▃▅▇▇ |
| 2018 | 1 | 0.99 | 0.73 | 0.15 | 0.37 | 0.61 | 0.75 | 0.85 | 0.97 | ▂▅▆▇▇ |
| 2019 | 1 | 0.99 | 0.73 | 0.15 | 0.28 | 0.61 | 0.75 | 0.85 | 0.97 | ▁▃▅▇▇ |
| 2020 | 1 | 0.99 | 0.73 | 0.15 | 0.39 | 0.62 | 0.74 | 0.84 | 0.97 | ▂▅▆▇▇ |
| 2021 | 1 | 0.99 | 0.73 | 0.15 | 0.34 | 0.61 | 0.74 | 0.84 | 0.97 | ▁▃▅▇▆ |
| 2022 | 1 | 0.99 | 0.74 | 0.15 | 0.38 | 0.62 | 0.76 | 0.85 | 0.97 | ▂▅▅▇▇ |
| 2023 | 0 | 1.00 | 0.74 | 0.15 | 0.39 | 0.62 | 0.76 | 0.86 | 0.97 | ▂▅▅▇▇ |
skim(broadband_clean)
| Name | broadband_clean |
| Number of rows | 206 |
| Number of columns | 27 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 26 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1 | 2 | 30 | 0 | 206 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| 1998 | 196 | 0.05 | 0.18 | 0.18 | 0 | 0.03 | 0.14 | 0.26 | 0.48 | ▇▃▃▁▃ |
| 1999 | 192 | 0.07 | 0.48 | 0.59 | 0 | 0.02 | 0.20 | 0.73 | 1.91 | ▇▃▁▁▁ |
| 2000 | 162 | 0.21 | 0.96 | 1.75 | 0 | 0.02 | 0.19 | 0.95 | 8.28 | ▇▁▁▁▁ |
| 2001 | 128 | 0.38 | 1.41 | 2.76 | 0 | 0.02 | 0.25 | 1.26 | 16.60 | ▇▁▁▁▁ |
| 2002 | 100 | 0.51 | 1.99 | 3.67 | 0 | 0.02 | 0.29 | 2.44 | 22.00 | ▇▁▁▁▁ |
| 2003 | 81 | 0.61 | 2.72 | 4.66 | 0 | 0.03 | 0.40 | 3.40 | 23.50 | ▇▁▁▁▁ |
| 2004 | 63 | 0.69 | 3.74 | 5.84 | 0 | 0.06 | 0.61 | 4.89 | 25.00 | ▇▁▁▁▁ |
| 2005 | 39 | 0.81 | 4.87 | 7.62 | 0 | 0.07 | 0.82 | 6.69 | 29.50 | ▇▁▁▁▁ |
| 2006 | 40 | 0.81 | 6.43 | 9.26 | 0 | 0.11 | 1.67 | 10.18 | 37.60 | ▇▁▁▁▁ |
| 2007 | 26 | 0.87 | 7.28 | 10.39 | 0 | 0.13 | 1.81 | 9.56 | 45.70 | ▇▁▁▁▁ |
| 2008 | 18 | 0.91 | 8.40 | 11.28 | 0 | 0.14 | 2.51 | 12.55 | 53.60 | ▇▁▁▁▁ |
| 2009 | 15 | 0.93 | 9.59 | 12.05 | 0 | 0.22 | 3.72 | 18.10 | 62.90 | ▇▂▂▁▁ |
| 2010 | 10 | 0.95 | 10.09 | 12.28 | 0 | 0.31 | 4.28 | 19.27 | 62.90 | ▇▂▂▁▁ |
| 2011 | 17 | 0.92 | 10.80 | 12.10 | 0 | 0.51 | 5.14 | 20.90 | 46.90 | ▇▂▂▁▁ |
| 2012 | 10 | 0.95 | 11.21 | 12.39 | 0 | 0.49 | 5.12 | 21.73 | 46.80 | ▇▂▂▂▁ |
| 2013 | 7 | 0.97 | 11.87 | 13.09 | 0 | 0.62 | 6.28 | 21.25 | 63.10 | ▇▂▂▁▁ |
| 2014 | 7 | 0.97 | 12.60 | 13.43 | 0 | 0.92 | 7.16 | 22.65 | 54.70 | ▇▂▂▂▁ |
| 2015 | 6 | 0.97 | 13.17 | 13.81 | 0 | 0.79 | 8.07 | 23.52 | 49.90 | ▇▂▂▂▁ |
| 2016 | 11 | 0.95 | 13.65 | 14.06 | 0 | 0.77 | 8.55 | 25.35 | 49.90 | ▇▂▂▂▁ |
| 2017 | 10 | 0.95 | 14.54 | 14.78 | 0 | 1.15 | 9.48 | 27.20 | 62.20 | ▇▂▂▁▁ |
| 2018 | 25 | 0.88 | 15.51 | 14.65 | 0 | 1.50 | 11.50 | 28.50 | 54.50 | ▇▃▃▂▁ |
| 2019 | 4 | 0.98 | 15.43 | 15.43 | 0 | 1.20 | 10.15 | 28.37 | 75.70 | ▇▃▂▁▁ |
| 2020 | 6 | 0.97 | 16.37 | 15.40 | 0 | 1.66 | 11.60 | 29.38 | 58.10 | ▇▂▃▂▁ |
| 2021 | 3 | 0.99 | 16.78 | 15.65 | 0 | 1.65 | 11.60 | 30.55 | 59.90 | ▇▂▃▂▁ |
| 2022 | 2 | 0.99 | 17.40 | 16.00 | 0 | 2.08 | 12.90 | 30.15 | 61.00 | ▇▂▃▂▁ |
| 2023 | 49 | 0.76 | 19.24 | 15.90 | 0 | 2.97 | 17.20 | 31.90 | 55.90 | ▇▃▅▃▁ |
skim(gdp_clean)
| Name | gdp_clean |
| Number of rows | 193 |
| Number of columns | 303 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 301 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| geo | 0 | 1 | 3 | 3 | 0 | 193 | 0 |
| name | 0 | 1 | 2 | 30 | 0 | 193 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| 1800 | 0 | 1 | 1031.02 | 577.84 | 354.84 | 636.43 | 918.88 | 1220.37 | 4789.23 | ▇▂▁▁▁ |
| 1801 | 0 | 1 | 1030.16 | 574.69 | 354.84 | 638.22 | 919.16 | 1220.37 | 4705.67 | ▇▂▁▁▁ |
| 1802 | 0 | 1 | 1032.78 | 585.38 | 354.84 | 638.87 | 920.99 | 1215.63 | 4965.51 | ▇▂▁▁▁ |
| 1803 | 0 | 1 | 1032.26 | 579.14 | 354.84 | 639.52 | 924.19 | 1209.39 | 4857.91 | ▇▂▁▁▁ |
| 1804 | 0 | 1 | 1035.23 | 589.37 | 354.84 | 640.17 | 925.92 | 1203.18 | 5089.13 | ▇▂▁▁▁ |
| 1805 | 0 | 1 | 1035.10 | 582.67 | 354.84 | 640.82 | 924.17 | 1197.00 | 4789.23 | ▇▂▁▁▁ |
| 1806 | 0 | 1 | 1036.27 | 583.78 | 354.84 | 642.62 | 925.22 | 1191.59 | 4825.86 | ▇▂▁▁▁ |
| 1807 | 0 | 1 | 1036.19 | 576.65 | 354.84 | 643.27 | 925.92 | 1201.89 | 4421.80 | ▇▂▁▁▁ |
| 1808 | 0 | 1 | 1027.36 | 533.24 | 354.84 | 643.93 | 927.01 | 1187.63 | 3720.13 | ▇▃▁▁▁ |
| 1809 | 0 | 1 | 1027.92 | 537.90 | 354.84 | 644.58 | 927.01 | 1188.66 | 3787.66 | ▇▃▁▁▁ |
| 1810 | 0 | 1 | 1029.69 | 545.78 | 354.84 | 645.23 | 927.01 | 1189.70 | 3981.11 | ▇▃▁▁▁ |
| 1811 | 0 | 1 | 1026.78 | 542.91 | 354.84 | 647.05 | 927.01 | 1191.76 | 3888.39 | ▇▃▁▁▁ |
| 1812 | 0 | 1 | 1025.44 | 535.81 | 354.84 | 647.27 | 921.63 | 1192.79 | 3670.91 | ▇▃▁▁▁ |
| 1813 | 0 | 1 | 1031.54 | 546.30 | 354.84 | 647.27 | 924.88 | 1194.86 | 3809.41 | ▇▃▁▁▁ |
| 1814 | 0 | 1 | 1034.93 | 549.76 | 354.84 | 647.27 | 927.01 | 1195.89 | 3667.47 | ▇▃▁▁▁ |
| 1815 | 0 | 1 | 1036.35 | 558.36 | 354.84 | 647.65 | 923.13 | 1196.92 | 3993.70 | ▇▃▁▁▁ |
| 1816 | 0 | 1 | 1033.94 | 543.37 | 354.84 | 651.50 | 923.13 | 1198.98 | 3720.13 | ▇▃▁▁▁ |
| 1817 | 0 | 1 | 1033.52 | 542.78 | 354.84 | 652.17 | 923.13 | 1200.02 | 3723.56 | ▇▃▁▁▁ |
| 1818 | 0 | 1 | 1038.22 | 548.05 | 354.84 | 652.45 | 929.73 | 1201.05 | 3664.04 | ▇▃▁▁▁ |
| 1819 | 0 | 1 | 1038.56 | 545.00 | 354.84 | 652.45 | 925.62 | 1204.16 | 3551.86 | ▇▃▁▁▁ |
| 1820 | 0 | 1 | 1044.51 | 559.75 | 354.84 | 652.45 | 936.33 | 1211.32 | 3784.23 | ▇▃▁▁▁ |
| 1821 | 0 | 1 | 1050.96 | 565.81 | 354.84 | 652.45 | 931.55 | 1218.51 | 3786.52 | ▇▃▁▁▁ |
| 1822 | 0 | 1 | 1055.37 | 569.17 | 354.84 | 652.45 | 938.64 | 1239.66 | 3794.53 | ▇▃▁▁▁ |
| 1823 | 0 | 1 | 1061.23 | 575.77 | 354.84 | 654.66 | 944.26 | 1244.24 | 3844.89 | ▇▃▁▁▁ |
| 1824 | 0 | 1 | 1068.13 | 586.03 | 354.84 | 656.88 | 945.31 | 1240.36 | 3997.13 | ▇▃▁▁▁ |
| 1825 | 0 | 1 | 1072.69 | 586.86 | 355.66 | 659.09 | 953.25 | 1247.73 | 4030.33 | ▇▃▁▁▁ |
| 1826 | 0 | 1 | 1078.36 | 586.57 | 356.99 | 660.57 | 957.35 | 1255.15 | 3765.91 | ▇▃▁▁▁ |
| 1827 | 0 | 1 | 1085.43 | 598.88 | 358.97 | 667.62 | 955.80 | 1262.55 | 4005.14 | ▇▃▁▁▁ |
| 1828 | 0 | 1 | 1090.69 | 602.41 | 360.30 | 677.62 | 958.98 | 1268.28 | 3961.65 | ▇▃▁▁▁ |
| 1829 | 0 | 1 | 1096.83 | 605.06 | 361.62 | 688.73 | 963.13 | 1276.37 | 3941.04 | ▇▃▁▁▁ |
| 1830 | 0 | 1 | 1103.09 | 609.60 | 362.95 | 694.81 | 966.09 | 1285.24 | 4063.52 | ▇▃▁▁▁ |
| 1831 | 0 | 1 | 1108.51 | 620.20 | 364.93 | 697.09 | 966.09 | 1292.88 | 4057.80 | ▇▃▁▁▁ |
| 1832 | 0 | 1 | 1117.13 | 636.11 | 366.26 | 700.53 | 966.09 | 1301.13 | 4134.49 | ▇▃▁▁▁ |
| 1833 | 0 | 1 | 1122.99 | 640.14 | 367.58 | 702.82 | 967.04 | 1326.88 | 4119.61 | ▇▃▁▁▁ |
| 1834 | 0 | 1 | 1129.59 | 643.30 | 369.57 | 705.11 | 970.07 | 1333.74 | 4165.40 | ▇▃▁▁▁ |
| 1835 | 0 | 1 | 1140.76 | 661.71 | 370.89 | 708.03 | 970.09 | 1340.64 | 4419.51 | ▇▂▁▁▁ |
| 1836 | 0 | 1 | 1144.69 | 666.73 | 372.22 | 709.69 | 970.44 | 1347.57 | 4506.50 | ▇▂▁▁▁ |
| 1837 | 0 | 1 | 1150.65 | 667.55 | 374.21 | 711.97 | 979.21 | 1354.54 | 4414.93 | ▇▂▁▁▁ |
| 1838 | 0 | 1 | 1155.41 | 673.01 | 375.53 | 714.26 | 982.28 | 1360.09 | 4536.26 | ▇▂▁▁▁ |
| 1839 | 0 | 1 | 1157.67 | 672.55 | 376.85 | 716.55 | 985.35 | 1362.01 | 4442.40 | ▇▂▁▁▁ |
| 1840 | 0 | 1 | 1166.84 | 682.31 | 378.84 | 718.84 | 992.42 | 1370.27 | 4599.22 | ▇▂▁▁▁ |
| 1841 | 0 | 1 | 1170.18 | 678.84 | 380.17 | 722.28 | 995.85 | 1379.55 | 4457.28 | ▇▃▁▁▁ |
| 1842 | 0 | 1 | 1172.71 | 674.57 | 381.49 | 722.93 | 1000.43 | 1387.81 | 4358.84 | ▇▃▁▁▁ |
| 1843 | 0 | 1 | 1177.78 | 681.43 | 383.48 | 722.93 | 995.85 | 1397.09 | 4521.38 | ▇▃▁▁▁ |
| 1844 | 0 | 1 | 1188.78 | 704.36 | 384.80 | 724.30 | 1000.88 | 1404.36 | 4859.06 | ▇▂▁▁▁ |
| 1845 | 0 | 1 | 1194.94 | 712.34 | 385.58 | 724.30 | 1011.51 | 1411.62 | 5021.60 | ▇▂▁▁▁ |
| 1846 | 0 | 1 | 1199.88 | 716.42 | 386.60 | 724.30 | 1007.16 | 1418.92 | 4960.93 | ▇▂▁▁▁ |
| 1847 | 0 | 1 | 1207.42 | 727.63 | 386.60 | 724.57 | 1017.88 | 1426.26 | 4846.47 | ▇▂▁▁▁ |
| 1848 | 0 | 1 | 1217.54 | 745.97 | 387.62 | 728.00 | 1021.26 | 1433.64 | 4991.84 | ▇▂▁▁▁ |
| 1849 | 0 | 1 | 1225.08 | 754.39 | 387.62 | 731.43 | 1028.19 | 1441.05 | 5061.66 | ▇▂▁▁▁ |
| 1850 | 0 | 1 | 1234.79 | 759.63 | 388.64 | 733.72 | 1029.04 | 1462.10 | 4958.64 | ▇▂▁▁▁ |
| 1851 | 0 | 1 | 1250.78 | 784.05 | 388.64 | 733.72 | 1050.41 | 1471.39 | 5124.62 | ▇▂▁▁▁ |
| 1852 | 0 | 1 | 1266.50 | 818.84 | 389.67 | 733.72 | 1055.96 | 1481.18 | 5295.17 | ▇▂▁▁▁ |
| 1853 | 0 | 1 | 1279.42 | 842.86 | 389.67 | 733.72 | 1062.66 | 1531.55 | 5492.05 | ▇▂▁▁▁ |
| 1854 | 0 | 1 | 1287.23 | 845.85 | 390.69 | 733.72 | 1064.50 | 1537.27 | 5619.11 | ▇▂▁▁▁ |
| 1855 | 0 | 1 | 1295.99 | 842.09 | 390.69 | 733.72 | 1066.34 | 1542.99 | 5434.82 | ▇▂▁▁▁ |
| 1856 | 0 | 1 | 1305.60 | 880.62 | 391.71 | 733.72 | 1068.18 | 1537.35 | 5738.15 | ▇▂▁▁▁ |
| 1857 | 0 | 1 | 1318.67 | 879.75 | 391.71 | 737.16 | 1070.02 | 1554.44 | 5745.02 | ▇▂▁▁▁ |
| 1858 | 0 | 1 | 1321.07 | 867.37 | 392.73 | 737.16 | 1071.87 | 1560.16 | 5584.77 | ▇▂▁▁▁ |
| 1859 | 0 | 1 | 1335.60 | 902.05 | 393.76 | 737.16 | 1073.72 | 1565.89 | 5758.76 | ▇▂▁▁▁ |
| 1860 | 0 | 1 | 1348.87 | 920.74 | 393.76 | 738.89 | 1075.57 | 1571.61 | 5821.71 | ▇▂▁▁▁ |
| 1861 | 0 | 1 | 1352.61 | 916.99 | 394.78 | 741.21 | 1077.43 | 1577.33 | 5750.74 | ▇▂▁▁▁ |
| 1862 | 0 | 1 | 1356.53 | 921.33 | 394.78 | 745.13 | 1080.38 | 1584.20 | 5453.13 | ▇▂▁▁▁ |
| 1863 | 0 | 1 | 1371.62 | 951.59 | 395.80 | 747.75 | 1094.29 | 1589.92 | 5912.14 | ▇▂▁▁▁ |
| 1864 | 0 | 1 | 1384.09 | 972.97 | 395.80 | 751.67 | 1102.51 | 1600.36 | 6015.16 | ▇▂▁▁▁ |
| 1865 | 0 | 1 | 1387.93 | 971.99 | 396.83 | 755.59 | 1111.08 | 1610.68 | 6104.44 | ▇▂▁▁▁ |
| 1866 | 0 | 1 | 1397.25 | 986.20 | 396.83 | 759.51 | 1118.76 | 1621.00 | 6239.51 | ▇▂▁▁▁ |
| 1867 | 0 | 1 | 1406.59 | 998.28 | 397.85 | 763.43 | 1116.82 | 1616.25 | 6208.60 | ▇▂▁▁▁ |
| 1868 | 0 | 1 | 1420.72 | 1024.89 | 397.85 | 766.05 | 1114.85 | 1642.67 | 6451.27 | ▇▂▁▁▁ |
| 1869 | 0 | 1 | 1435.94 | 1043.49 | 398.87 | 769.97 | 1112.12 | 1683.79 | 6450.13 | ▇▂▁▁▁ |
| 1870 | 0 | 1 | 1449.71 | 1064.61 | 398.87 | 772.93 | 1110.13 | 1733.01 | 6672.19 | ▇▂▁▁▁ |
| 1871 | 0 | 1 | 1460.78 | 1074.88 | 399.89 | 774.80 | 1123.38 | 1702.10 | 6635.56 | ▇▂▁▁▁ |
| 1872 | 0 | 1 | 1488.56 | 1127.48 | 399.89 | 777.66 | 1137.78 | 1767.43 | 6603.51 | ▇▂▁▁▁ |
| 1873 | 0 | 1 | 1509.13 | 1166.71 | 400.92 | 781.22 | 1148.75 | 1802.83 | 6990.40 | ▇▂▁▁▁ |
| 1874 | 0 | 1 | 1516.84 | 1166.80 | 400.92 | 783.86 | 1156.10 | 1787.95 | 7012.15 | ▇▂▁▁▁ |
| 1875 | 0 | 1 | 1525.90 | 1178.69 | 401.94 | 785.62 | 1160.68 | 1820.36 | 7550.14 | ▇▂▁▁▁ |
| 1876 | 0 | 1 | 1534.54 | 1174.66 | 401.94 | 787.38 | 1155.16 | 1838.35 | 7310.91 | ▇▂▁▁▁ |
| 1877 | 0 | 1 | 1545.32 | 1194.07 | 402.96 | 789.14 | 1157.89 | 1856.52 | 7363.56 | ▇▂▁▁▁ |
| 1878 | 0 | 1 | 1557.62 | 1223.79 | 402.96 | 790.90 | 1172.13 | 1851.34 | 7803.11 | ▇▂▁▁▁ |
| 1879 | 0 | 1 | 1558.78 | 1192.05 | 403.98 | 793.55 | 1176.70 | 1874.89 | 7672.62 | ▇▂▁▁▁ |
| 1880 | 0 | 1 | 1587.75 | 1247.70 | 403.98 | 795.31 | 1180.14 | 1898.43 | 7817.99 | ▇▂▁▁▁ |
| 1881 | 0 | 1 | 1599.41 | 1269.20 | 404.19 | 797.07 | 1184.72 | 1921.97 | 8128.19 | ▇▂▁▁▁ |
| 1882 | 0 | 1 | 1617.21 | 1274.21 | 407.46 | 799.01 | 1206.47 | 1941.33 | 7506.64 | ▇▂▁▁▁ |
| 1883 | 0 | 1 | 1636.07 | 1312.21 | 410.74 | 804.71 | 1193.82 | 1964.14 | 8164.82 | ▇▂▁▁▁ |
| 1884 | 0 | 1 | 1649.45 | 1320.25 | 414.01 | 810.45 | 1201.84 | 1981.96 | 7853.47 | ▇▂▁▁▁ |
| 1885 | 0 | 1 | 1660.16 | 1330.85 | 417.28 | 820.72 | 1209.92 | 2008.50 | 8068.67 | ▇▂▁▁▁ |
| 1886 | 0 | 1 | 1672.23 | 1341.53 | 420.55 | 822.04 | 1225.43 | 2028.36 | 7898.11 | ▇▂▁▁▁ |
| 1887 | 0 | 1 | 1702.50 | 1368.49 | 424.15 | 834.06 | 1247.67 | 2060.56 | 8450.98 | ▇▂▁▁▁ |
| 1888 | 0 | 1 | 1718.11 | 1383.64 | 427.43 | 839.37 | 1237.37 | 2071.82 | 8217.47 | ▇▂▁▁▁ |
| 1889 | 0 | 1 | 1725.67 | 1418.23 | 431.03 | 827.58 | 1242.76 | 2086.13 | 8661.60 | ▇▂▁▁▁ |
| 1890 | 0 | 1 | 1735.22 | 1407.29 | 434.30 | 840.35 | 1241.57 | 2109.75 | 8133.91 | ▇▂▁▁▁ |
| 1891 | 0 | 1 | 1733.27 | 1414.84 | 437.90 | 831.02 | 1245.29 | 2045.50 | 8513.94 | ▇▂▁▁▁ |
| 1892 | 0 | 1 | 1754.77 | 1417.07 | 441.50 | 834.06 | 1249.02 | 2129.06 | 8383.45 | ▇▂▁▁▁ |
| 1893 | 0 | 1 | 1774.36 | 1401.71 | 445.10 | 853.44 | 1276.50 | 2172.92 | 7822.57 | ▇▂▁▁▁ |
| 1894 | 0 | 1 | 1808.43 | 1424.64 | 448.70 | 864.21 | 1339.21 | 2230.93 | 7842.03 | ▇▂▁▁▁ |
| 1895 | 0 | 1 | 1822.92 | 1458.62 | 452.30 | 842.87 | 1293.71 | 2237.80 | 8195.72 | ▇▂▁▁▁ |
| 1896 | 0 | 1 | 1851.96 | 1500.33 | 455.90 | 843.28 | 1362.59 | 2245.81 | 8254.10 | ▇▂▁▁▁ |
| 1897 | 0 | 1 | 1857.44 | 1496.98 | 459.50 | 855.06 | 1339.21 | 2260.11 | 8477.31 | ▇▂▁▁▁ |
| 1898 | 0 | 1 | 1890.47 | 1540.10 | 463.43 | 851.62 | 1373.81 | 2322.58 | 8584.91 | ▇▂▁▁▁ |
| 1899 | 0 | 1 | 1911.94 | 1583.57 | 467.03 | 847.69 | 1405.22 | 2305.01 | 9110.30 | ▇▂▁▁▁ |
| 1900 | 0 | 1 | 1919.82 | 1595.02 | 467.63 | 853.68 | 1407.12 | 2330.88 | 9200.73 | ▇▂▁▁▁ |
| 1901 | 0 | 1 | 1940.09 | 1612.20 | 468.22 | 858.92 | 1438.20 | 2323.65 | 10038.62 | ▇▂▁▁▁ |
| 1902 | 0 | 1 | 1972.19 | 1640.42 | 468.82 | 864.91 | 1466.07 | 2357.99 | 9940.18 | ▇▂▁▁▁ |
| 1903 | 0 | 1 | 1989.23 | 1672.38 | 469.41 | 870.90 | 1467.45 | 2397.50 | 10234.35 | ▇▂▁▁▁ |
| 1904 | 0 | 1 | 2015.84 | 1690.26 | 470.00 | 878.63 | 1481.34 | 2426.67 | 9917.28 | ▇▂▁▁▁ |
| 1905 | 0 | 1 | 2028.11 | 1739.07 | 470.60 | 896.26 | 1449.20 | 2462.15 | 10440.39 | ▇▂▁▁▁ |
| 1906 | 0 | 1 | 2064.44 | 1823.08 | 471.19 | 899.70 | 1415.94 | 2487.33 | 11423.65 | ▇▂▁▁▁ |
| 1907 | 0 | 1 | 2078.36 | 1848.10 | 471.79 | 894.86 | 1443.41 | 2493.71 | 11390.45 | ▇▂▁▁▁ |
| 1908 | 0 | 1 | 2088.06 | 1800.08 | 472.39 | 900.85 | 1486.91 | 2572.04 | 10274.42 | ▇▂▁▁▁ |
| 1909 | 0 | 1 | 2126.31 | 1859.93 | 472.98 | 906.84 | 1516.34 | 2608.67 | 11215.32 | ▇▂▁▁▁ |
| 1910 | 0 | 1 | 2168.72 | 1913.10 | 473.58 | 912.29 | 1578.48 | 2568.38 | 11031.03 | ▇▂▁▁▁ |
| 1911 | 0 | 1 | 2182.55 | 1944.77 | 474.18 | 914.58 | 1534.77 | 2593.77 | 11144.35 | ▇▂▁▁▁ |
| 1912 | 0 | 1 | 2217.93 | 1982.40 | 474.78 | 918.01 | 1587.97 | 2595.14 | 11420.21 | ▇▂▁▁▁ |
| 1913 | 0 | 1 | 2240.61 | 1998.74 | 475.38 | 921.45 | 1625.41 | 2607.52 | 11570.16 | ▇▂▁▁▁ |
| 1914 | 0 | 1 | 2198.21 | 1880.37 | 475.98 | 932.89 | 1590.96 | 2646.44 | 10411.77 | ▇▂▁▁▁ |
| 1915 | 0 | 1 | 2200.53 | 1873.98 | 476.59 | 943.20 | 1630.98 | 2561.74 | 10489.61 | ▇▂▁▁▁ |
| 1916 | 0 | 1 | 2219.99 | 1951.31 | 477.19 | 954.64 | 1621.78 | 2586.53 | 11700.66 | ▇▂▁▁▁ |
| 1917 | 0 | 1 | 2186.84 | 1888.13 | 477.79 | 924.73 | 1585.39 | 2602.66 | 11182.13 | ▇▂▁▁▁ |
| 1918 | 0 | 1 | 2132.14 | 1858.98 | 478.40 | 927.64 | 1543.27 | 2577.76 | 11985.67 | ▇▁▁▁▁ |
| 1919 | 0 | 1 | 2155.18 | 1884.34 | 479.00 | 891.69 | 1560.16 | 2601.49 | 11961.64 | ▇▁▁▁▁ |
| 1920 | 0 | 1 | 2190.00 | 1925.58 | 480.44 | 897.41 | 1614.47 | 2633.85 | 11621.67 | ▇▂▁▁▁ |
| 1921 | 0 | 1 | 2199.18 | 1869.64 | 480.44 | 907.90 | 1637.92 | 2712.83 | 11074.53 | ▇▂▁▁▁ |
| 1922 | 0 | 1 | 2290.23 | 1976.44 | 479.82 | 935.18 | 1640.29 | 2761.57 | 11457.99 | ▇▂▁▁▁ |
| 1923 | 0 | 1 | 2349.85 | 2068.71 | 480.13 | 973.82 | 1663.18 | 2801.69 | 12672.47 | ▇▂▁▁▁ |
| 1924 | 0 | 1 | 2425.85 | 2135.42 | 480.48 | 1010.43 | 1700.96 | 2851.33 | 12736.57 | ▇▂▁▁▁ |
| 1925 | 0 | 1 | 2495.65 | 2183.07 | 481.18 | 1034.31 | 1713.55 | 2890.25 | 12762.89 | ▇▂▁▁▁ |
| 1926 | 0 | 1 | 2544.58 | 2216.93 | 481.87 | 1055.99 | 1775.20 | 2939.31 | 13332.93 | ▇▂▁▁▁ |
| 1927 | 0 | 1 | 2594.21 | 2275.56 | 482.55 | 1077.67 | 1824.58 | 2964.65 | 13200.15 | ▇▂▁▁▁ |
| 1928 | 0 | 1 | 2679.13 | 2361.32 | 483.16 | 1096.58 | 1878.38 | 3031.51 | 13107.44 | ▇▂▁▁▁ |
| 1929 | 0 | 1 | 2739.06 | 2414.21 | 483.83 | 1074.58 | 1957.36 | 3138.64 | 13683.20 | ▇▂▁▁▁ |
| 1930 | 0 | 1 | 2716.03 | 2344.06 | 484.48 | 1080.67 | 1984.33 | 3128.15 | 12242.08 | ▇▂▁▁▁ |
| 1931 | 0 | 1 | 2646.27 | 2218.31 | 485.13 | 1099.60 | 1988.82 | 3131.56 | 11509.50 | ▇▂▁▁▁ |
| 1932 | 0 | 1 | 2591.01 | 2138.16 | 485.77 | 1132.32 | 1932.31 | 3104.77 | 11162.67 | ▇▂▁▁▁ |
| 1933 | 0 | 1 | 2653.73 | 2183.49 | 486.41 | 1129.42 | 1956.21 | 3180.71 | 11456.84 | ▇▂▁▁▁ |
| 1934 | 0 | 1 | 2733.24 | 2265.20 | 487.03 | 1158.39 | 1979.39 | 3255.40 | 11443.11 | ▇▂▁▁▁ |
| 1935 | 0 | 1 | 2820.12 | 2345.05 | 487.65 | 1172.55 | 2109.60 | 3421.73 | 11081.40 | ▇▂▁▁▁ |
| 1936 | 0 | 1 | 2911.97 | 2454.22 | 488.25 | 1219.06 | 2153.09 | 3479.22 | 12096.71 | ▇▃▁▁▁ |
| 1937 | 0 | 1 | 3021.21 | 2582.38 | 488.85 | 1183.57 | 2206.89 | 3537.67 | 12928.87 | ▇▂▁▁▁ |
| 1938 | 0 | 1 | 3061.19 | 2623.31 | 489.43 | 1188.14 | 2232.08 | 3569.03 | 12048.63 | ▇▃▁▁▁ |
| 1939 | 0 | 1 | 3144.17 | 2741.05 | 490.01 | 1219.61 | 2251.53 | 3596.93 | 12786.93 | ▇▂▁▁▁ |
| 1940 | 0 | 1 | 3139.40 | 2755.33 | 490.57 | 1230.50 | 2331.66 | 3653.58 | 13866.34 | ▇▂▁▁▁ |
| 1941 | 0 | 1 | 3190.26 | 2890.90 | 488.47 | 1250.15 | 2319.17 | 3662.23 | 16170.12 | ▇▂▁▁▁ |
| 1942 | 0 | 1 | 3230.57 | 3064.59 | 486.37 | 1240.13 | 2328.94 | 3691.41 | 18857.01 | ▇▂▁▁▁ |
| 1943 | 0 | 1 | 3271.56 | 3235.95 | 484.26 | 1266.79 | 2307.09 | 3696.57 | 21989.43 | ▇▁▁▁▁ |
| 1944 | 0 | 1 | 3298.81 | 3409.61 | 482.16 | 1306.10 | 2258.95 | 3679.89 | 25641.64 | ▇▁▁▁▁ |
| 1945 | 0 | 1 | 3280.53 | 3510.75 | 480.05 | 1282.92 | 2243.71 | 3637.04 | 29898.19 | ▇▁▁▁▁ |
| 1946 | 0 | 1 | 3395.65 | 3768.57 | 477.94 | 1203.11 | 2240.71 | 3727.89 | 34860.21 | ▇▁▁▁▁ |
| 1947 | 0 | 1 | 3537.65 | 4106.10 | 476.51 | 1226.89 | 2368.37 | 3969.36 | 40645.98 | ▇▁▁▁▁ |
| 1948 | 0 | 1 | 3725.53 | 4485.56 | 475.77 | 1315.50 | 2542.00 | 4116.00 | 47389.30 | ▇▁▁▁▁ |
| 1949 | 0 | 1 | 3888.06 | 4956.80 | 475.71 | 1324.76 | 2655.06 | 4335.98 | 55247.60 | ▇▁▁▁▁ |
| 1950 | 0 | 1 | 4068.84 | 5569.89 | 476.00 | 1334.08 | 2574.85 | 4473.90 | 64408.73 | ▇▁▁▁▁ |
| 1951 | 0 | 1 | 4193.45 | 5676.11 | 477.62 | 1472.36 | 2681.02 | 4611.08 | 65466.77 | ▇▁▁▁▁ |
| 1952 | 0 | 1 | 4309.94 | 5804.11 | 479.01 | 1444.80 | 2755.08 | 4633.55 | 66539.37 | ▇▁▁▁▁ |
| 1953 | 0 | 1 | 4471.93 | 5967.12 | 480.68 | 1499.46 | 2801.68 | 4815.28 | 67628.24 | ▇▁▁▁▁ |
| 1954 | 0 | 1 | 4643.08 | 6135.83 | 483.20 | 1525.27 | 2929.55 | 5345.79 | 68731.90 | ▇▁▁▁▁ |
| 1955 | 0 | 1 | 4842.55 | 6340.54 | 486.86 | 1538.96 | 3093.28 | 5456.09 | 69851.00 | ▇▁▁▁▁ |
| 1956 | 0 | 1 | 5033.51 | 6531.21 | 490.20 | 1599.21 | 3136.35 | 5472.07 | 70987.18 | ▇▁▁▁▁ |
| 1957 | 0 | 1 | 5210.38 | 6728.59 | 493.35 | 1655.23 | 3344.68 | 5860.88 | 72138.97 | ▇▁▁▁▁ |
| 1958 | 0 | 1 | 5357.76 | 6918.39 | 497.44 | 1639.12 | 3398.18 | 5899.94 | 73306.97 | ▇▁▁▁▁ |
| 1959 | 0 | 1 | 5553.99 | 7202.17 | 499.83 | 1667.91 | 3440.83 | 6301.18 | 74490.74 | ▇▁▁▁▁ |
| 1960 | 0 | 1 | 5840.49 | 7552.96 | 501.78 | 1712.91 | 3605.66 | 6470.70 | 75691.87 | ▇▁▁▁▁ |
| 1961 | 0 | 1 | 6117.62 | 7970.15 | 503.02 | 1745.31 | 3715.60 | 7237.65 | 76894.78 | ▇▁▁▁▁ |
| 1962 | 0 | 1 | 6396.45 | 8477.40 | 515.07 | 1788.41 | 3785.59 | 7549.14 | 78114.33 | ▇▁▁▁▁ |
| 1963 | 0 | 1 | 6693.92 | 9128.60 | 519.27 | 1774.25 | 3844.69 | 7884.42 | 79351.04 | ▇▁▁▁▁ |
| 1964 | 0 | 1 | 7166.34 | 10029.67 | 530.35 | 1816.37 | 4033.75 | 8393.89 | 80603.46 | ▇▁▁▁▁ |
| 1965 | 0 | 1 | 7475.32 | 10373.07 | 540.95 | 1852.56 | 4198.33 | 8585.44 | 81875.08 | ▇▁▁▁▁ |
| 1966 | 0 | 1 | 7898.15 | 11311.56 | 551.82 | 1910.64 | 4403.45 | 9188.80 | 85437.76 | ▇▁▁▁▁ |
| 1967 | 0 | 1 | 8269.11 | 12075.41 | 583.91 | 1913.99 | 4457.18 | 9471.34 | 95119.00 | ▇▁▁▁▁ |
| 1968 | 0 | 1 | 8683.79 | 12784.17 | 603.65 | 1926.72 | 4596.16 | 10450.80 | 100793.96 | ▇▁▁▁▁ |
| 1969 | 0 | 1 | 9090.14 | 13308.88 | 630.14 | 2044.24 | 4612.47 | 10635.22 | 99178.96 | ▇▁▁▁▁ |
| 1970 | 0 | 1 | 9600.98 | 14362.53 | 622.55 | 2008.56 | 4773.29 | 10987.22 | 92944.06 | ▇▁▁▁▁ |
| 1971 | 0 | 1 | 9936.71 | 14951.05 | 635.77 | 2063.64 | 4981.62 | 11481.84 | 102400.18 | ▇▁▁▁▁ |
| 1972 | 0 | 1 | 10291.80 | 15390.89 | 638.35 | 2187.80 | 5273.91 | 12414.28 | 107043.88 | ▇▁▁▁▁ |
| 1973 | 0 | 1 | 10719.57 | 15839.91 | 636.58 | 2291.34 | 5448.15 | 13137.90 | 114683.01 | ▇▁▁▁▁ |
| 1974 | 0 | 1 | 11005.68 | 15920.69 | 635.59 | 2498.57 | 5827.08 | 13592.08 | 106784.26 | ▇▁▁▁▁ |
| 1975 | 0 | 1 | 10870.43 | 15277.52 | 620.28 | 2502.81 | 5885.61 | 13216.74 | 103552.16 | ▇▁▁▁▁ |
| 1976 | 0 | 1 | 11213.74 | 15928.71 | 613.76 | 2578.10 | 5999.57 | 13890.74 | 117272.23 | ▇▁▁▁▁ |
| 1977 | 0 | 1 | 11402.43 | 15919.20 | 589.08 | 2544.72 | 6079.33 | 14139.69 | 124432.05 | ▇▁▁▁▁ |
| 1978 | 0 | 1 | 11601.66 | 15989.35 | 566.00 | 2613.44 | 6103.22 | 14232.69 | 127347.21 | ▇▁▁▁▁ |
| 1979 | 0 | 1 | 12050.49 | 17330.92 | 551.79 | 2605.31 | 6646.94 | 14397.51 | 149806.43 | ▇▁▁▁▁ |
| 1980 | 0 | 1 | 12002.78 | 16860.78 | 547.28 | 2550.49 | 6086.50 | 15259.07 | 133984.05 | ▇▁▁▁▁ |
| 1981 | 0 | 1 | 11747.60 | 15414.14 | 559.98 | 2603.92 | 6376.58 | 15120.16 | 103485.07 | ▇▁▁▁▁ |
| 1982 | 0 | 1 | 11570.06 | 14736.51 | 518.36 | 2622.00 | 6319.58 | 15187.47 | 103779.56 | ▇▁▁▁▁ |
| 1983 | 0 | 1 | 11493.47 | 14326.03 | 444.13 | 2567.55 | 6109.49 | 15264.61 | 100724.40 | ▇▂▁▁▁ |
| 1984 | 0 | 1 | 11708.66 | 14435.25 | 414.08 | 2462.10 | 6560.62 | 15473.56 | 97898.49 | ▇▂▁▁▁ |
| 1985 | 0 | 1 | 11738.30 | 14186.36 | 396.66 | 2607.10 | 6408.94 | 15442.21 | 93169.30 | ▇▂▁▁▁ |
| 1986 | 0 | 1 | 11814.08 | 13815.28 | 389.61 | 2772.19 | 6600.42 | 15840.02 | 89190.03 | ▇▂▁▁▁ |
| 1987 | 0 | 1 | 11982.56 | 14040.89 | 433.69 | 2757.78 | 6702.59 | 15307.35 | 92345.64 | ▇▂▁▁▁ |
| 1988 | 0 | 1 | 12204.70 | 14244.79 | 469.74 | 2807.87 | 6813.60 | 15528.20 | 97581.75 | ▇▂▁▁▁ |
| 1989 | 0 | 1 | 12466.78 | 14757.63 | 494.59 | 2806.69 | 6915.93 | 15591.19 | 102690.61 | ▇▂▁▁▁ |
| 1990 | 0 | 1 | 12538.44 | 15106.53 | 491.67 | 2814.66 | 6703.43 | 16008.23 | 106474.19 | ▇▂▁▁▁ |
| 1991 | 0 | 1 | 12435.10 | 15269.92 | 499.77 | 2714.96 | 7032.45 | 15214.72 | 108602.20 | ▇▂▁▁▁ |
| 1992 | 0 | 1 | 12440.87 | 15582.14 | 449.51 | 2689.95 | 6903.56 | 14748.06 | 111141.86 | ▇▂▁▁▁ |
| 1993 | 0 | 1 | 12501.16 | 15803.07 | 475.69 | 2654.67 | 6522.42 | 14910.93 | 111208.47 | ▇▂▁▁▁ |
| 1994 | 0 | 1 | 12793.78 | 16392.11 | 476.12 | 2609.85 | 6619.48 | 14997.29 | 114837.99 | ▇▂▁▁▁ |
| 1995 | 0 | 1 | 13168.74 | 16933.20 | 460.36 | 2747.53 | 6562.11 | 14689.07 | 118539.89 | ▇▂▁▁▁ |
| 1996 | 0 | 1 | 13557.72 | 17335.59 | 500.17 | 2825.32 | 6758.75 | 15510.48 | 121244.46 | ▇▂▁▁▁ |
| 1997 | 0 | 1 | 14094.75 | 18030.63 | 542.78 | 3005.81 | 7079.87 | 16470.05 | 125465.22 | ▇▂▁▁▁ |
| 1998 | 0 | 1 | 14459.48 | 18562.14 | 585.28 | 3009.40 | 7301.64 | 17121.58 | 131460.65 | ▇▂▁▁▁ |
| 1999 | 0 | 1 | 14800.51 | 19192.96 | 634.38 | 3029.82 | 7481.41 | 16962.52 | 137399.19 | ▇▂▁▁▁ |
| 2000 | 0 | 1 | 15375.68 | 20049.69 | 623.86 | 3063.37 | 7822.52 | 18099.07 | 144346.88 | ▇▂▁▁▁ |
| 2001 | 0 | 1 | 15626.64 | 20329.44 | 656.81 | 2995.55 | 8015.49 | 17923.57 | 148492.49 | ▇▂▁▁▁ |
| 2002 | 0 | 1 | 15887.49 | 20577.19 | 661.56 | 3110.64 | 8022.86 | 18330.26 | 151397.53 | ▇▂▁▁▁ |
| 2003 | 0 | 1 | 16319.53 | 21039.45 | 683.69 | 3162.97 | 8287.74 | 19845.48 | 154387.03 | ▇▂▁▁▁ |
| 2004 | 0 | 1 | 17033.77 | 21914.58 | 713.52 | 3213.12 | 8700.97 | 20777.53 | 159591.41 | ▇▂▁▁▁ |
| 2005 | 0 | 1 | 17569.16 | 22331.03 | 738.94 | 3306.87 | 8922.88 | 21808.48 | 163814.55 | ▇▂▁▁▁ |
| 2006 | 0 | 1 | 18275.94 | 23089.88 | 758.23 | 3445.07 | 9566.50 | 23464.50 | 172260.42 | ▇▂▁▁▁ |
| 2007 | 0 | 1 | 18966.69 | 24128.88 | 784.18 | 3543.62 | 9816.02 | 25594.17 | 195076.50 | ▇▁▁▁▁ |
| 2008 | 0 | 1 | 19100.62 | 23773.91 | 810.21 | 3518.02 | 10314.15 | 25769.52 | 194605.84 | ▇▁▁▁▁ |
| 2009 | 0 | 1 | 18297.01 | 22224.04 | 801.94 | 3543.01 | 10258.97 | 24057.73 | 177404.79 | ▇▂▁▁▁ |
| 2010 | 0 | 1 | 18639.26 | 22624.93 | 804.35 | 3735.79 | 10636.24 | 24452.28 | 181527.16 | ▇▁▁▁▁ |
| 2011 | 0 | 1 | 19006.41 | 23334.12 | 807.66 | 3632.28 | 10933.21 | 25020.78 | 194719.44 | ▇▁▁▁▁ |
| 2012 | 0 | 1 | 19188.38 | 23231.10 | 506.72 | 3756.93 | 11098.89 | 26141.69 | 194719.44 | ▇▁▁▁▁ |
| 2013 | 0 | 1 | 19297.48 | 23176.97 | 624.18 | 3950.88 | 11361.25 | 26275.42 | 194065.70 | ▇▁▁▁▁ |
| 2014 | 0 | 1 | 19529.30 | 23279.43 | 614.46 | 4012.55 | 11767.16 | 26497.57 | 195343.02 | ▇▁▁▁▁ |
| 2015 | 0 | 1 | 19814.46 | 23548.18 | 594.92 | 4163.92 | 12030.38 | 27545.36 | 197056.17 | ▇▁▁▁▁ |
| 2016 | 0 | 1 | 20071.81 | 23788.31 | 501.11 | 4297.84 | 12336.90 | 27993.19 | 199583.99 | ▇▁▁▁▁ |
| 2017 | 0 | 1 | 20411.26 | 23994.38 | 458.77 | 4458.47 | 12497.82 | 28895.47 | 201458.07 | ▇▂▁▁▁ |
| 2018 | 0 | 1 | 20774.51 | 24299.11 | 435.41 | 4464.81 | 13218.92 | 30185.56 | 203540.00 | ▇▂▁▁▁ |
| 2019 | 0 | 1 | 21051.17 | 24559.09 | 425.94 | 4775.38 | 13215.57 | 30674.02 | 205749.33 | ▇▁▁▁▁ |
| 2020 | 0 | 1 | 19942.80 | 24111.13 | 386.68 | 4615.54 | 12407.79 | 26953.15 | 207844.68 | ▇▁▁▁▁ |
| 2021 | 0 | 1 | 21053.87 | 25312.17 | 395.80 | 4745.64 | 13045.93 | 30416.79 | 210111.00 | ▇▁▁▁▁ |
| 2022 | 0 | 1 | 21823.61 | 25998.30 | 364.70 | 4591.33 | 13148.07 | 33117.84 | 213893.00 | ▇▂▁▁▁ |
| 2023 | 0 | 1 | 22007.15 | 25865.53 | 354.18 | 4733.08 | 13416.93 | 33781.34 | 217743.07 | ▇▂▁▁▁ |
| 2024 | 0 | 1 | 22369.00 | 26193.73 | 363.48 | 4964.38 | 13397.20 | 34182.07 | 221662.45 | ▇▂▁▁▁ |
| 2025 | 0 | 1 | 22884.93 | 26732.70 | 377.24 | 5050.45 | 13623.32 | 34548.44 | 225652.37 | ▇▂▁▁▁ |
| 2026 | 0 | 1 | 23408.74 | 27396.00 | 385.82 | 5076.46 | 13999.36 | 35411.54 | 229714.11 | ▇▁▁▁▁ |
| 2027 | 0 | 1 | 23899.94 | 27910.17 | 392.67 | 5191.12 | 14362.97 | 36425.99 | 233848.97 | ▇▁▁▁▁ |
| 2028 | 0 | 1 | 24436.57 | 28430.09 | 398.18 | 5351.39 | 14954.81 | 37528.62 | 238058.25 | ▇▁▁▁▁ |
| 2029 | 0 | 1 | 25012.21 | 28973.00 | 400.98 | 5549.51 | 15659.08 | 38730.86 | 242343.30 | ▇▁▁▁▁ |
| 2030 | 0 | 1 | 24606.39 | 28759.43 | 391.26 | 5454.60 | 15394.08 | 38220.76 | 244056.21 | ▇▁▁▁▁ |
| 2031 | 0 | 1 | 24878.01 | 28538.57 | 398.30 | 5583.59 | 15752.15 | 38828.88 | 237220.87 | ▇▁▁▁▁ |
| 2032 | 0 | 1 | 25154.90 | 28342.85 | 405.47 | 5716.06 | 16116.88 | 39438.83 | 230818.57 | ▇▂▁▁▁ |
| 2033 | 0 | 1 | 25436.83 | 28169.74 | 412.77 | 5852.09 | 16488.29 | 40050.38 | 224812.89 | ▇▂▁▁▁ |
| 2034 | 0 | 1 | 25723.60 | 28016.99 | 420.20 | 5991.77 | 16866.38 | 40663.32 | 219171.21 | ▇▂▁▁▁ |
| 2035 | 0 | 1 | 26015.02 | 27882.62 | 427.76 | 6135.18 | 17251.16 | 41277.42 | 213864.26 | ▇▂▁▁▁ |
| 2036 | 0 | 1 | 26310.92 | 27764.82 | 435.46 | 6282.42 | 17642.62 | 41892.48 | 208865.67 | ▇▂▁▁▁ |
| 2037 | 0 | 1 | 26611.13 | 27662.00 | 443.30 | 6433.57 | 18040.75 | 42508.26 | 204151.69 | ▇▂▁▁▁ |
| 2038 | 0 | 1 | 26915.52 | 27572.71 | 451.28 | 6588.72 | 18445.55 | 43124.54 | 199700.83 | ▇▂▁▁▁ |
| 2039 | 0 | 1 | 27223.93 | 27495.66 | 459.40 | 6747.96 | 18856.98 | 43741.12 | 195493.63 | ▇▂▁▁▁ |
| 2040 | 0 | 1 | 27536.26 | 27429.67 | 467.67 | 6911.39 | 19275.02 | 44357.76 | 191512.42 | ▇▂▁▁▁ |
| 2041 | 0 | 1 | 27852.37 | 27373.70 | 476.09 | 7079.10 | 19699.63 | 44974.26 | 187741.14 | ▇▂▁▁▁ |
| 2042 | 0 | 1 | 28172.16 | 27326.78 | 484.66 | 7251.18 | 20130.78 | 45590.40 | 184165.16 | ▇▃▁▁▁ |
| 2043 | 0 | 1 | 28495.50 | 27288.04 | 493.38 | 7427.72 | 20568.43 | 46205.96 | 180771.10 | ▇▃▁▁▁ |
| 2044 | 0 | 1 | 28822.31 | 27256.70 | 502.26 | 7608.82 | 21012.50 | 46820.73 | 177546.77 | ▇▃▁▁▁ |
| 2045 | 0 | 1 | 29152.47 | 27232.04 | 511.30 | 7794.58 | 21462.96 | 47434.51 | 174480.96 | ▇▃▁▁▁ |
| 2046 | 0 | 1 | 29485.91 | 27213.40 | 520.51 | 7985.09 | 21919.72 | 48047.08 | 171563.42 | ▇▃▁▁▁ |
| 2047 | 0 | 1 | 29822.53 | 27200.16 | 529.88 | 8180.43 | 22382.73 | 48658.25 | 168784.71 | ▇▃▁▁▁ |
| 2048 | 0 | 1 | 30162.23 | 27191.80 | 539.41 | 8380.72 | 22851.89 | 49267.81 | 166136.15 | ▇▃▁▁▁ |
| 2049 | 0 | 1 | 30504.95 | 27187.80 | 549.12 | 8586.05 | 23327.13 | 49875.56 | 163609.74 | ▇▃▁▁▁ |
| 2050 | 0 | 1 | 30850.58 | 27187.70 | 559.01 | 8796.50 | 23808.34 | 50481.32 | 161198.11 | ▇▃▁▁▁ |
| 2051 | 0 | 1 | 31199.07 | 27191.06 | 569.07 | 9012.17 | 24295.44 | 51084.89 | 158894.42 | ▇▃▁▁▁ |
| 2052 | 0 | 1 | 31550.34 | 27197.48 | 579.31 | 9233.16 | 24788.32 | 51686.08 | 156692.38 | ▇▃▂▁▁ |
| 2053 | 0 | 1 | 31904.30 | 27206.61 | 589.74 | 9459.56 | 25286.86 | 52284.72 | 154586.12 | ▇▃▂▁▁ |
| 2054 | 0 | 1 | 32260.89 | 27218.11 | 600.36 | 9691.46 | 25790.94 | 52880.63 | 152570.24 | ▇▃▂▁▁ |
| 2055 | 0 | 1 | 32620.03 | 27231.65 | 611.16 | 9928.96 | 26300.45 | 53473.62 | 150639.67 | ▇▃▂▁▁ |
| 2056 | 0 | 1 | 32981.66 | 27246.96 | 622.16 | 10172.14 | 26815.25 | 54063.54 | 148789.75 | ▇▃▂▁▁ |
| 2057 | 0 | 1 | 33345.70 | 27263.76 | 633.36 | 10421.09 | 27335.21 | 54650.22 | 147016.10 | ▇▃▂▁▁ |
| 2058 | 0 | 1 | 33712.08 | 27281.80 | 644.76 | 10675.90 | 27860.18 | 55233.49 | 145314.66 | ▇▅▂▁▁ |
| 2059 | 0 | 1 | 34080.75 | 27300.85 | 656.37 | 10936.66 | 28390.01 | 55813.21 | 143681.63 | ▇▃▃▁▁ |
| 2060 | 0 | 1 | 34451.62 | 27320.68 | 668.18 | 11203.44 | 28924.56 | 56389.23 | 142113.48 | ▇▅▃▁▁ |
| 2061 | 0 | 1 | 34824.64 | 27341.09 | 680.21 | 11476.34 | 29463.66 | 56961.39 | 140606.89 | ▇▅▃▁▁ |
| 2062 | 0 | 1 | 35199.75 | 27361.89 | 692.45 | 11755.43 | 30007.15 | 57553.49 | 139158.76 | ▇▅▃▁▁ |
| 2063 | 0 | 1 | 35576.87 | 27382.91 | 704.92 | 12040.78 | 30554.86 | 58207.61 | 137766.19 | ▇▅▃▁▁ |
| 2064 | 0 | 1 | 35955.94 | 27403.97 | 717.61 | 12332.48 | 31106.62 | 58858.50 | 136426.45 | ▇▅▃▁▁ |
| 2065 | 0 | 1 | 36336.89 | 27424.93 | 730.52 | 12630.59 | 31662.25 | 59505.98 | 135136.99 | ▇▅▃▁▁ |
| 2066 | 0 | 1 | 36719.67 | 27445.62 | 743.67 | 12935.19 | 32221.56 | 60149.87 | 133895.41 | ▇▅▃▁▁ |
| 2067 | 0 | 1 | 37104.21 | 27465.93 | 757.06 | 13246.33 | 32784.38 | 60789.99 | 132699.46 | ▇▅▅▁▁ |
| 2068 | 0 | 1 | 37490.45 | 27485.71 | 770.69 | 13564.09 | 33350.52 | 61426.18 | 131547.00 | ▇▅▅▁▁ |
| 2069 | 0 | 1 | 37878.32 | 27504.84 | 784.56 | 13888.51 | 33919.77 | 62058.27 | 130436.05 | ▇▅▅▁▁ |
| 2070 | 0 | 1 | 38267.76 | 27523.21 | 798.68 | 14219.65 | 34491.95 | 62645.76 | 129364.72 | ▇▆▅▁▁ |
| 2071 | 0 | 1 | 38658.70 | 27540.72 | 813.06 | 14557.57 | 35066.85 | 63169.53 | 128331.22 | ▇▆▅▂▁ |
| 2072 | 0 | 1 | 39051.10 | 27557.26 | 827.69 | 14902.30 | 35644.28 | 63688.11 | 127333.90 | ▇▆▅▂▁ |
| 2073 | 0 | 1 | 39444.87 | 27572.74 | 842.59 | 15253.90 | 36224.03 | 64201.42 | 126371.15 | ▇▆▅▂▁ |
| 2074 | 0 | 1 | 39839.97 | 27587.06 | 857.76 | 15612.39 | 36805.89 | 64709.40 | 125441.49 | ▇▆▅▂▁ |
| 2075 | 0 | 1 | 40236.32 | 27600.15 | 873.20 | 15977.82 | 37389.66 | 65211.97 | 124543.50 | ▇▇▆▂▁ |
| 2076 | 0 | 1 | 40633.86 | 27611.92 | 888.92 | 16350.20 | 37975.13 | 65709.08 | 123675.84 | ▇▇▆▂▁ |
| 2077 | 0 | 1 | 41032.55 | 27622.29 | 904.92 | 16729.56 | 38562.09 | 66200.68 | 122837.23 | ▇▇▆▃▁ |
| 2078 | 0 | 1 | 41432.31 | 27631.20 | 921.20 | 17115.91 | 39160.71 | 66686.69 | 122026.48 | ▇▆▆▃▁ |
| 2079 | 0 | 1 | 41833.08 | 27638.57 | 937.79 | 17509.27 | 39773.01 | 67167.10 | 121242.45 | ▇▆▆▃▁ |
| 2080 | 0 | 1 | 42234.80 | 27644.35 | 954.67 | 17909.65 | 40397.15 | 67641.84 | 120484.05 | ▇▆▅▃▁ |
| 2081 | 0 | 1 | 42637.41 | 27648.47 | 971.85 | 18317.03 | 41039.65 | 68110.88 | 119750.25 | ▇▆▅▃▁ |
| 2082 | 0 | 1 | 43040.85 | 27650.88 | 989.34 | 18731.41 | 41684.07 | 68574.20 | 119040.08 | ▇▆▅▅▁ |
| 2083 | 0 | 1 | 43445.06 | 27651.52 | 1007.15 | 19152.79 | 42330.19 | 69031.76 | 118352.60 | ▇▆▅▅▁ |
| 2084 | 0 | 1 | 43849.98 | 27650.34 | 1025.28 | 19581.13 | 42977.76 | 69483.54 | 117686.94 | ▇▆▅▅▁ |
| 2085 | 0 | 1 | 44255.54 | 27647.31 | 1043.74 | 20016.42 | 43626.56 | 69929.52 | 117042.26 | ▇▆▅▅▁ |
| 2086 | 0 | 1 | 44661.69 | 27642.36 | 1062.52 | 20458.63 | 44276.35 | 70369.68 | 116417.74 | ▇▆▅▆▁ |
| 2087 | 0 | 1 | 45068.37 | 27635.46 | 1081.65 | 20907.70 | 44926.90 | 70804.02 | 115812.64 | ▇▆▆▆▁ |
| 2088 | 0 | 1 | 45475.51 | 27626.57 | 1101.12 | 21363.61 | 45577.99 | 71232.52 | 115226.22 | ▇▇▆▆▁ |
| 2089 | 0 | 1 | 45883.06 | 27615.65 | 1120.94 | 21826.29 | 46229.37 | 71655.18 | 114657.79 | ▇▇▇▇▁ |
| 2090 | 0 | 1 | 46290.96 | 27602.67 | 1141.11 | 22295.68 | 46880.81 | 72072.01 | 114106.68 | ▇▇▇▇▁ |
| 2091 | 0 | 1 | 46699.15 | 27587.59 | 1161.65 | 22771.73 | 47532.10 | 72477.70 | 113572.27 | ▇▇▇▇▁ |
| 2092 | 0 | 1 | 47107.57 | 27570.38 | 1182.56 | 23254.35 | 48182.99 | 72862.40 | 113053.96 | ▇▇▇▇▁ |
| 2093 | 0 | 1 | 47516.16 | 27551.01 | 1203.85 | 23743.47 | 48833.27 | 73241.40 | 112551.15 | ▇▇▇▇▁ |
| 2094 | 0 | 1 | 47924.86 | 27529.46 | 1225.52 | 24239.00 | 49482.71 | 73614.73 | 112063.31 | ▇▇▇▇▁ |
| 2095 | 0 | 1 | 48333.62 | 27505.71 | 1247.58 | 24740.84 | 50131.09 | 73982.40 | 111589.91 | ▇▇▇▇▁ |
| 2096 | 0 | 1 | 48742.37 | 27479.72 | 1270.04 | 25248.90 | 50778.20 | 74344.45 | 111130.43 | ▇▆▇▇▂ |
| 2097 | 0 | 1 | 49151.07 | 27451.48 | 1292.90 | 25763.07 | 51423.81 | 74700.90 | 110684.40 | ▇▆▇▇▂ |
| 2098 | 0 | 1 | 49559.65 | 27420.97 | 1316.17 | 26283.23 | 52067.72 | 75051.78 | 110251.36 | ▇▆▇▇▂ |
| 2099 | 0 | 1 | 49968.05 | 27388.16 | 1339.86 | 26809.26 | 52709.72 | 75397.13 | 109830.86 | ▆▆▇▇▂ |
| 2100 | 0 | 1 | 50376.22 | 27353.05 | 1363.98 | 27341.03 | 53349.60 | 75736.97 | 109422.48 | ▆▆▇▇▂ |
cat(“HDI: Number of empty records removed:”, sum(is.na(hdi_clean))) hdi_clean <- hdi_clean %>% drop_na() cat(“Broadband: Number of empty records removed:”, sum(is.na(broadband_clean))) broadband_clean <- broadband_clean %>% drop_na() cat(“GDP: Number of empty records removed:”, sum(is.na(gdp_clean))) gdp_clean <- gdp_clean %>% drop_na()
#create a long table for easy analysis
broadband_long <- broadband_clean %>%
pivot_longer(cols=-country, names_to="year", values_to="broadband") %>%
mutate(year=as.numeric(year))
hdi_long <- hdi_clean %>%
pivot_longer(cols=-country, names_to="year", values_to="hdi") %>%
mutate(year=as.numeric(year))
gdp_long <- gdp_clean[-1.] %>%
rename(country=name) %>%
pivot_longer(cols=-country, names_to="year", values_to="gdp") %>%
mutate(year=as.numeric(year))
#Join the HDI & Broadband data by year and country
merged_data <- merge(broadband_long, hdi_long, by = c("country","year"))
#Join the GDP dataset
merged_data <- merge(merged_data, gdp_long, by = c("country", "year"))
#remove the missing values
merged_data <- na.omit(merged_data)
#visualize the relationship between broadband subscription and HDI over the time
ggplot(merged_data, aes(x = broadband, y = hdi)) +
geom_point(alpha = 0.6, color = "steelblue") +
geom_smooth(method = "lm", color = "darkred") +
theme_minimal() +
labs(title = "Relationship between Broadband Subscriptions and HDI",
x = "Broadband subscriptions per 100 people",
y = "Human Development Index")
#Looking at the first research question #What is the relationship between HDI and broadband subscriptions, based on the latest available data, which is 2023.
#Pull the data for 2023 only
data_2023 <- merged_data %>% filter(year==2023)
#visualize the relationship between broadband subscription and HDI in the year of 2023
ggplot(data_2023, aes(x = broadband, y = hdi)) +
geom_point(alpha = 0.6, color = "steelblue") +
geom_smooth(method = "lm", color = "darkred") +
theme_minimal() +
labs(title = "Relationship between Broadband Subscriptions and HDI in the year 2023",
x = "Broadband subscriptions per 100 people",
y = "Human Development Index")
#Test the skewness and distribution of the data
#Histogram of HDI in 2023. Histogram show that the HDI of 2023 is mildy left skewed
ggplot(data_2023,aes(x=hdi))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of HDI in 2023",
x = "HDI",
y = "Frequency")
# Box plot of HDI in 2023
ggplot(data_2023, aes(y=hdi)) +
geom_boxplot() +
labs(title = "Box plot of HDI in 2023",
y="Count")
#Histogram of broadband in 2023. Histogram show that the broadband data is right skewed
ggplot(data_2023,aes(x=broadband))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Broadband in 2023",
x = "Broadband",
y = "Frequency")
# Box plot of broadband in 2023
ggplot(data_2023, aes(y=broadband)) +
geom_boxplot() +
labs(title = "Box plot of Broadband in 2023",
y="Count")
Need to transform the broadband dataset. Option 1: Log Transform
#log transformed the broadband
data_2023 <- data_2023 %>%
mutate(broadband = ifelse(broadband == 0, NA, broadband)) %>%
mutate(log_broadband = log(broadband))
#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=log_broadband))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Log Tranformed Broadband in 2023",
x = "Log Transformed Broadband",
y = "Frequency")
shapiro.test(data_2023$log_broadband)
##
## Shapiro-Wilk normality test
##
## data: data_2023$log_broadband
## W = 0.8112, p-value = 1.241e-12
qqnorm(data_2023$log_broadband, main = "Q-Q Plot for log transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")
# Add a reference line
qqline(data_2023$log_broadband, col = "steelblue", lwd = 2)
Option 2: Square root Transform
#Square root transform of the broadband dataset.
data_2023 <- data_2023 %>%
mutate(sqrt_broadband = sqrt(broadband))
#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=sqrt_broadband))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Square rootTranformed Broadband in 2023",
x = "Square root Transformed Broadband",
y = "Frequency")
shapiro.test(data_2023$sqrt_broadband)
##
## Shapiro-Wilk normality test
##
## data: data_2023$sqrt_broadband
## W = 0.9317, p-value = 1.306e-06
qqnorm(data_2023$sqrt_broadband, main = "Q-Q Plot for Square root transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")
# Add a reference line
qqline(data_2023$sqrt_broadband, col = "steelblue", lwd = 2)
#Create a scatter plot of square root broadband vs hdi of 2023
ggplot(data_2023, aes(x = sqrt_broadband, y = hdi)) +
geom_point(color = "steelblue") + # Add scatter points
geom_smooth(method = "lm", color = "darkred", linetype = "solid") + # Adds a linear regression line
labs(title = "Scatter Plot with Line of Best Fit of Square root Broadband vs HDI",
x = "Square root Broadband Subscription per 100",
y = "HDI")
#Create a model for 2023 data
model_2023 <- lm(hdi ~ sqrt_broadband, data=data_2023)
summary(model_2023)
##
## Call:
## lm(formula = hdi ~ sqrt_broadband, data = data_2023)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.184558 -0.036556 0.002638 0.036693 0.254059
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.536983 0.010067 53.34 <2e-16 ***
## sqrt_broadband 0.060655 0.002339 25.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06115 on 148 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.8197, Adjusted R-squared: 0.8184
## F-statistic: 672.7 on 1 and 148 DF, p-value: < 2.2e-16
#We want to do a comparision between 2011 and 2023. #In 2011, the United Nations General Assembly highlighed Internet access as a basic human right
data_2011 <- merged_data %>% filter(year==2011)
#visualize the relationship between broadband subscription and HDI in the year of 2011
ggplot(data_2011, aes(x = broadband, y = hdi)) +
geom_point(alpha = 0.6, color = "steelblue") +
geom_smooth(method = "lm", color = "darkred") +
theme_minimal() +
labs(title = "Relationship between Broadband Subscriptions and HDI in the year 2011",
x = "Broadband subscriptions per 100 people",
y = "Human Development Index")
## `geom_smooth()` using formula = 'y ~ x'
#Test the skewness and distribution of the data
#Histogram of HDI in 2011. Histogram show that the HDI of 2011 is mildy left skewed
ggplot(data_2011,aes(x=hdi))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of HDI in 2011",
x = "HDI",
y = "Frequency")
# Box plot of HDI in 2011
ggplot(data_2011, aes(y=hdi)) +
geom_boxplot() +
labs(title = "Box plot of HDI in 2011",
y="Count")
#Histogram of broadband in 2011. Histogram show that the broadband data is severe right skewed
ggplot(data_2011,aes(x=broadband))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Broadband in 2011",
x = "Broadband",
y = "Frequency")
# Box plot of broadband in 2023
ggplot(data_2011, aes(y=broadband)) +
geom_boxplot() +
labs(title = "Box plot of Broadband in 2011",
y="Count")
Need to transform the braodband dataset. Option 1: Log Transform
data_2011 <- data_2011 %>%
filter(broadband != 0) %>%
mutate(log_broadband = log(broadband))
#Testing the skewness of log transform broadband values
ggplot(data_2011,aes(x=log_broadband))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Log Tranformed Broadband in 2011",
x = "Log Transformed Broadband",
y = "Frequency")
shapiro.test(data_2011$log_broadband)
##
## Shapiro-Wilk normality test
##
## data: data_2011$log_broadband
## W = 0.90542, p-value = 3.125e-09
qqnorm(data_2011$log_broadband, main = "Q-Q Plot for log transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")
# Add a reference line
qqline(data_2011$log_broadband, col = "steelblue", lwd = 2)
Option 2: Square root Transform
#Square root transform the broadband dataset.
data_2011 <- data_2011 %>%
mutate(sqrt_broadband = sqrt(broadband))
#Testing the skewness of log transform broadband values
ggplot(data_2011,aes(x=sqrt_broadband))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Square root Tranformed Broadband in 2011",
x = "Square root Transformed Broadband",
y = "Frequency")
shapiro.test(data_2011$sqrt_broadband)
##
## Shapiro-Wilk normality test
##
## data: data_2011$sqrt_broadband
## W = 0.91094, p-value = 7.037e-09
qqnorm(data_2011$sqrt_broadband, main = "Q-Q Plot for Square root transformed broadband", xlab = "Theoretical Quantiles", ylab = "log transformed broadband")
# Add a reference line
qqline(data_2011$sqrt_broadband, col = "steelblue", lwd = 2)
#Create a scatter plot of square root broadband vs hdi of 2011
ggplot(data_2011, aes(x = sqrt_broadband, y = hdi)) +
geom_point(color = "steelblue") + # Add scatter points
geom_smooth(method = "lm", color = "darkred", linetype = "solid") + # Adds a linear regression line
labs(title = "Scatter Plot with Line of Best Fit of Square root Broadband vs HDI",
x = "Square root Broadband Subscription per 100",
y = "HDI")
#Create a model for 2011 data
model_2011 <- lm(hdi ~ sqrt_broadband, data=data_2011)
summary(model_2011)
##
## Call:
## lm(formula = hdi ~ sqrt_broadband, data = data_2011)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.201315 -0.038654 -0.004344 0.044943 0.227565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.539498 0.008574 62.92 <2e-16 ***
## sqrt_broadband 0.070482 0.002766 25.48 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07026 on 175 degrees of freedom
## Multiple R-squared: 0.7877, Adjusted R-squared: 0.7865
## F-statistic: 649.4 on 1 and 175 DF, p-value: < 2.2e-16
#Question 2: Finding the outliers countries in 2023
data_2023 <- na.omit(data_2023) %>%
mutate(
fitted_hdi = fitted(model_2023,),
residuals = resid(model_2023),
std_resid = rstandard(model_2023)
)
outliers <- data_2023 %>%
filter(abs(std_resid) > 3)
outliers
## country year broadband hdi gdp log_broadband sqrt_broadband
## 1 Kuwait 2023 1.010 0.852 46714.534 0.009950331 1.0049876
## 2 Somalia 2023 0.723 0.404 1129.263 -0.324346057 0.8502941
## fitted_hdi residuals std_resid
## 1 0.5979406 0.2540594 4.19170
## 2 0.5885576 -0.1845576 -3.04697
#Question 3: looking at GDP as the controlling factor #Testing the skewness of GDP
#histogram of GDP in 2023
ggplot(data_2023,aes(x=gdp))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of GDP in 2023",
x = "GDP",
y = "Frequency")
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
# Box plot of GDP in 2023
ggplot(data_2023, aes(y=gdp)) +
geom_boxplot() +
labs(title = "Box plot of GDP in 2023",
y="Count")
The GDP dataset is right skewed and need to be transformed. Option 1:
Log transform
data_2023 <- data_2023 %>%
filter(broadband != 0) %>%
mutate(log_gdp = log(gdp))
#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=log_gdp))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Log Tranformed GDP in 2023",
x = "Log Transformed GDP",
y = "Frequency")
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
shapiro.test(data_2023$log_gdp)
##
## Shapiro-Wilk normality test
##
## data: data_2023$log_gdp
## W = 0.96318, p-value = 0.0004862
qqnorm(data_2023$log_gdp, main = "Q-Q Plot for log transformed GDP", xlab = "Theoretical Quantiles", ylab = "log transformed GDP")
# Add a reference line
qqline(data_2023$log_gdp, col = "steelblue", lwd = 2)
Option 2: Square root transform gdp
data_2023 <- data_2023 %>%
filter(broadband != 0) %>%
mutate(sqrt_gdp = sqrt(gdp))
#Testing the skewness of log transform broadband values
ggplot(data_2023,aes(x=sqrt_gdp))+
geom_histogram(colour="white", fill="steelblue")+
labs(title="Histogram of Square root Tranformed GDP in 2023",
x = "Square root Transformed GDP",
y = "Frequency")
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
shapiro.test(data_2023$sqrt_gdp)
##
## Shapiro-Wilk normality test
##
## data: data_2023$sqrt_gdp
## W = 0.96378, p-value = 0.0005542
qqnorm(data_2023$sqrt_gdp, main = "Q-Q Plot for Square root transformed GDP", xlab = "Theoretical Quantiles", ylab = "Square root transformed GDP")
# Add a reference line
qqline(data_2023$sqrt_gdp, col = "steelblue", lwd = 2)
#Create a scatter plot of log GDP vs hdi of 2023
ggplot(data_2023, aes(x = log_gdp, y = hdi)) +
geom_point(color = "steelblue") + # Add scatter points
geom_smooth(method = "lm", color = "darkred", linetype = "solid") + # Adds a linear regression line
labs(title = "Scatter Plot with Line of Best Fit of Log GDP vs HDI",
x = "Log GDP",
y = "HDI")
## `geom_smooth()` using formula = 'y ~ x'
#Create a multi regression with gdp as the control variable
multi_reg <- lm (hdi ~ sqrt_broadband + log_gdp, data = data_2023)
summary(multi_reg)
##
## Call:
## lm(formula = hdi ~ sqrt_broadband + log_gdp, data = data_2023)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.116794 -0.016716 0.005263 0.018851 0.115077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.147792 0.037583 -3.932 0.000129 ***
## sqrt_broadband 0.019361 0.002586 7.486 6.02e-12 ***
## log_gdp 0.087961 0.004775 18.422 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03373 on 147 degrees of freedom
## Multiple R-squared: 0.9455, Adjusted R-squared: 0.9448
## F-statistic: 1275 on 2 and 147 DF, p-value: < 2.2e-16
#Checking assumptions
plot(multi_reg, which = 1)
dwtest(multi_reg)
##
## Durbin-Watson test
##
## data: multi_reg
## DW = 1.8992, p-value = 0.2702
## alternative hypothesis: true autocorrelation is greater than 0
res_model <- residuals(multi_reg)
shapiro.test(res_model)
##
## Shapiro-Wilk normality test
##
## data: res_model
## W = 0.97612, p-value = 0.01025
plot(multi_reg, which = 2)
pred_model <- fitted(multi_reg)
plot(pred_model, res_model,
xlab = "Predicted Values (Fitted)",
ylab = "Residuals",
main = "Residuals vs Predicted Values",
col = "steelblue")
abline(h = 0, col = "red", lwd = 2)
vif(multi_reg)
## sqrt_broadband log_gdp
## 4.019114 4.019114